architecture: FasterRCNN
max_iters: 11000
snapshot_iter: 1000
use_gpu: true
log_smooth_window: 20
save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
weights: output/faster_rcnn_r50_vd_fpn_2x/model_final
metric: COCO
num_classes: 7

FasterRCNN:
  backbone: ResNet
  fpn: FPN
  rpn_head: FPNRPNHead
  roi_extractor: FPNRoIAlign
  bbox_head: BBoxHead
  bbox_assigner: BBoxAssigner

ResNet:
  depth: 50
  feature_maps: [2, 3, 4, 5]
  freeze_at: 2
  norm_type: bn
  variant: d

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

FPNRPNHead:
  anchor_generator:
    anchor_sizes: [8, 16, 32, 64, 128]
    aspect_ratios: [0.5, 1.0, 2.0]
    stride: [16.0, 16.0]
    variance: [1.0, 1.0, 1.0, 1.0]
  anchor_start_size: 8
  max_level: 6
  min_level: 2
  num_chan: 256
  rpn_target_assign:
    rpn_batch_size_per_im: 256
    rpn_fg_fraction: 0.5
    rpn_negative_overlap: 0.3
    rpn_positive_overlap: 0.7
    rpn_straddle_thresh: 0.0
  train_proposal:
    min_size: 0.0
    nms_thresh: 0.7
    post_nms_top_n: 2000
    pre_nms_top_n: 2000
  test_proposal:
    min_size: 0.0
    nms_thresh: 0.7
    post_nms_top_n: 1000
    pre_nms_top_n: 1000

FPNRoIAlign:
  canconical_level: 4
  canonical_size: 224
  max_level: 5
  min_level: 2
  box_resolution: 7
  sampling_ratio: 2

BBoxAssigner:
  batch_size_per_im: 512
  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
  bg_thresh_hi: 0.5
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5

BBoxHead:
  head: TwoFCHead
  nms:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05

TwoFCHead:
  mlp_dim: 1024

LearningRate:
  base_lr: 0.0025
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [7122, 9800]
  - !LinearWarmup
    start_factor: 0.333
    steps: 500

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

TrainReader:
  inputs_def:
    fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
  dataset:
    !COCODataSet
    image_dir: images
    anno_path: Annotations/train.json
    dataset_dir: /home/aistudio/work/PCB_DATASET
  sample_transforms:
  - !DecodeImage
    to_rgb: 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]
  - !ResizeImage
    target_size: 800
    max_size: 1333
    interp: 1
    use_cv2: true
  - !Permute
    to_bgr: false
    channel_first: true
  batch_transforms:
  - !PadBatch
    pad_to_stride: 32
    use_padded_im_info: false
  batch_size: 2
  shuffle: true
  worker_num: 2
  use_process: false

EvalReader:
  inputs_def:
    fields: ['image', 'im_info', 'im_id', 'im_shape']
  dataset:
    !COCODataSet
    image_dir: images
    anno_path: Annotations/val.json
    dataset_dir: /home/aistudio/work/PCB_DATASET
  sample_transforms:
  - !DecodeImage
    to_rgb: true
    with_mixup: false
  - !NormalizeImage
    is_channel_first: false
    is_scale: true
    mean: [0.485,0.456,0.406]
    std: [0.229, 0.224,0.225]
  - !ResizeImage
    interp: 1
    max_size: 1333
    target_size: 800
    use_cv2: true
  - !Permute
    channel_first: true
    to_bgr: false
  batch_transforms:
  - !PadBatch
    pad_to_stride: 32
    use_padded_im_info: true
  batch_size: 1
  shuffle: false
  drop_empty: false
  worker_num: 2

TestReader:
  inputs_def:
    # set image_shape if needed
    fields: ['image', 'im_info', 'im_id', 'im_shape']
  dataset:
    !ImageFolder
    anno_path: /home/aistudio/work/PCB_DATASET/Annotations/val.json
  sample_transforms:
  - !DecodeImage
    to_rgb: true
    with_mixup: false
  - !NormalizeImage
    is_channel_first: false
    is_scale: true
    mean: [0.485,0.456,0.406]
    std: [0.229, 0.224,0.225]
  - !ResizeImage
    interp: 1
    max_size: 1333
    target_size: 800
    use_cv2: true
  - !Permute
    channel_first: true
    to_bgr: false
  batch_transforms:
  - !PadBatch
    pad_to_stride: 32
    use_padded_im_info: true
  batch_size: 1
  shuffle: false