faster_rcnn_swin_tiny_fpn_3x_coco.yml

_BASE_: [
  '../datasets/coco_detection.yml',
  '../runtime.yml',
  '../faster_rcnn/_base_/faster_rcnn_r50_fpn.yml',
  '../faster_rcnn/_base_/faster_fpn_reader.yml',
]
weights: output/faster_rcnn_swin_tiny_fpn_3x_coco/model_final
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/swin_tiny_patch4_window7_224_22kto1k_pretrained.pdparams


FasterRCNN:
  backbone: SwinTransformer
  neck: FPN
  rpn_head: RPNHead
  bbox_head: BBoxHead
  bbox_post_process: BBoxPostProcess

SwinTransformer:
  arch: 'swin_T_224' # ['swin_T_224', 'swin_S_224', 'swin_B_224', 'swin_L_224', 'swin_B_384', 'swin_L_384']
  ape: false
  drop_path_rate: 0.1
  patch_norm: true
  out_indices: [0, 1, 2, 3]


worker_num: 2
TrainReader:
  sample_transforms:
    - Decode: {}
    - RandomResizeCrop: {resizes: [400, 500, 600], cropsizes: [[384, 600], ], prob: 0.5}
    - RandomResize: {target_size: [[480, 1333], [512, 1333], [544, 1333], [576, 1333], [608, 1333], [640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], keep_ratio: True, interp: 2}
    - RandomFlip: {prob: 0.5}
    - 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}
  batch_size: 2
  shuffle: true
  drop_last: true
  collate_batch: false

EvalReader:
  sample_transforms:
    - Decode: {}
    - Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
    - 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}
  batch_size: 1

TestReader:
  inputs_def:
    image_shape: [-1, 3, 640, 640] # TODO deploy: set fixes shape currently
  sample_transforms:
    - Decode: {}
    - Resize: {interp: 2, target_size: 640, keep_ratio: True}
    - Pad: {size: 640}
    - NormalizeImage: {is_scale: true, mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225]}
    - Permute: {}
  batch_size: 1


epoch: 36
LearningRate:
  base_lr: 0.0001
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [24, 33]
  - !LinearWarmup
    start_factor: 0.1
    steps: 1000

OptimizerBuilder:
  clip_grad_by_norm: 1.0
  optimizer:
    type: AdamW
    weight_decay: 0.05
    param_groups:
      - params: ['absolute_pos_embed', 'relative_position_bias_table', 'norm']
        weight_decay: 0.0