denseteacher_ppyoloe_plus_crn_s_coco_semi010.yml

_BASE_: [
  '../../ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml',
  '../_base_/coco_detection_percent_10.yml',
]
log_iter: 50
snapshot_epoch: 5
weights: output/denseteacher_ppyoloe_plus_crn_s_coco_semi010/model_final

epochs: &epochs 200
cosine_epochs: &cosine_epochs 240


### pretrain and warmup config, choose one and comment another
pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/ppyoloe_plus_crn_s_80e_coco_sup010.pdparams # mAP=35.3
semi_start_iters: 0
ema_start_iters: 0
use_warmup: &use_warmup False

# pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams
# semi_start_iters: 5000
# ema_start_iters: 3000
# use_warmup: &use_warmup True


### global config
use_simple_ema: True
ema_decay: 0.9996
ssod_method: DenseTeacher
DenseTeacher:
  train_cfg:
    sup_weight: 1.0
    unsup_weight: 1.0
    loss_weight: {distill_loss_cls: 1.0, distill_loss_iou: 2.5, distill_loss_dfl: 0., distill_loss_contrast: 0.1}
    contrast_loss:
      temperature: 0.2
      alpha: 0.9
      smooth_iter: 100
    concat_sup_data: True
    suppress: linear
    ratio: 0.01
  test_cfg:
    inference_on: teacher


### reader config
batch_size: &batch_size 8
worker_num: 2
SemiTrainReader:
  sample_transforms:
    - Decode: {}
    - RandomDistort: {}
    - RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
    - RandomFlip: {}
    - RandomCrop: {} # unsup will be fake gt_boxes
  weak_aug:
    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], is_scale: true, norm_type: none}
  strong_aug:
    - StrongAugImage: {transforms: [
        RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
        RandomErasingCrop: {},
        RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
        RandomGrayscale: {prob: 0.2},
      ]}
    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], is_scale: true, norm_type: none}
  sup_batch_transforms:
    - BatchRandomResize: {target_size: [640], random_size: True, random_interp: True, keep_ratio: False}
    - Permute: {}
    - PadGT: {}
  unsup_batch_transforms:
    - BatchRandomResize: {target_size: [640], random_size: True, random_interp: True, keep_ratio: False}
    - Permute: {}
  sup_batch_size: *batch_size
  unsup_batch_size: *batch_size
  shuffle: True
  drop_last: True
  collate_batch: True

EvalReader:
  sample_transforms:
    - Decode: {}
    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
    - Permute: {}
  batch_size: 2

TestReader:
  inputs_def:
    image_shape: [3, 640, 640]
  sample_transforms:
    - Decode: {}
    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
    - Permute: {}
  batch_size: 1


### model config
architecture: PPYOLOE
norm_type: sync_bn
ema_black_list: ['proj_conv.weight']
custom_black_list: ['reduce_mean']
PPYOLOE:
  backbone: CSPResNet
  neck: CustomCSPPAN
  yolo_head: PPYOLOEHead
  post_process: ~

eval_size: ~ # means None, but not str 'None'
PPYOLOEHead:
  fpn_strides: [32, 16, 8]
  grid_cell_scale: 5.0
  grid_cell_offset: 0.5
  static_assigner_epoch: -1 #
  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: 300
    score_threshold: 0.01
    nms_threshold: 0.7


### other config
epoch: *epochs
LearningRate:
  base_lr: 0.01
  schedulers:
  - !CosineDecay
    max_epochs: *cosine_epochs
    use_warmup: *use_warmup
  - !LinearWarmup
    start_factor: 0.001
    epochs: 3

OptimizerBuilder:
  optimizer:
    momentum: 0.9
    type: Momentum
  regularizer:
    factor: 0.0005 # dt-fcos 0.0001
    type: L2
  clip_grad_by_norm: 1.0 # dt-fcos clip_grad_by_value