# Runtime
use_gpu: true
use_xpu: false
use_mlu: false
use_npu: false
log_iter: 20
save_dir: output
print_flops: false
print_params: false
find_unused_parameters: True
use_ema: true
cycle_epoch: 20
snapshot_epoch: 2

# Dataset
metric: COCO
num_classes: 1

TrainDataset:
  name: COCODataSet
  image_dir: train2017
  anno_path: annotations/instances_train2017.json
  dataset_dir: dataset/coco
  data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']

EvalDataset:
  name: COCODataSet
  image_dir: val2017
  anno_path: annotations/instances_val2017.json
  dataset_dir: dataset/coco
  allow_empty: true

TestDataset:
  name: ImageFolder
  anno_path: annotations/instances_val2017.json # also support txt (like VOC's label_list.txt)
  dataset_dir: dataset/coco # if set, anno_path will be 'dataset_dir/anno_path'


# Reader
worker_num: 6
eval_height: &eval_height 640
eval_width: &eval_width 640
eval_size: &eval_size [*eval_height, *eval_width]

TrainReader:
  sample_transforms:
  - Decode: {}
  - RandomCrop: {}
  - RandomFlip: {prob: 0.5}
  - RandomDistort: {}
  batch_transforms:
  - BatchRandomResize: {target_size: [576, 608, 640, 672, 704], random_size: True, random_interp: True, keep_ratio: False}
  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
  - Permute: {}
  batch_size: 56
  shuffle: true
  drop_last: true
  collate_batch: false


EvalReader:
  sample_transforms:
  - Decode: {}
  - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
  - 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: 8
  shuffle: false


TestReader:
  inputs_def:
    image_shape: [1, 3, *eval_height, *eval_width]
  sample_transforms:
  - Decode: {}
  - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
  - Permute: {}
  batch_size: 1

# Model
architecture: PicoDet
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x2_5_ssld_pretrained.pdparams

PicoDet:
  backbone: LCNet
  neck: CSPPAN
  head: PicoHead

LCNet:
  scale: 2.5
  feature_maps: [3, 4, 5]

ESNet:
  scale: 1.0
  feature_maps: [4, 11, 14]
  act: hard_swish
  channel_ratio: [0.875, 0.5, 1.0, 0.625, 0.5, 0.75, 0.625, 0.625, 0.5, 0.625, 1.0, 0.625, 0.75]

CSPPAN:
  out_channels: 128
  use_depthwise: True
  num_csp_blocks: 1
  num_features: 4

PicoHead:
  conv_feat:
    name: PicoFeat
    feat_in: 128
    feat_out: 128
    num_convs: 4
    num_fpn_stride: 4
    norm_type: bn
    share_cls_reg: True
  fpn_stride: [8, 16, 32, 64]
  feat_in_chan: 128
  prior_prob: 0.01
  reg_max: 7
  cell_offset: 0.5
  loss_class:
    name: VarifocalLoss
    use_sigmoid: True
    iou_weighted: True
    loss_weight: 1.0
  loss_dfl:
    name: DistributionFocalLoss
    loss_weight: 0.25
  loss_bbox:
    name: GIoULoss
    loss_weight: 2.0
  assigner:
    name: SimOTAAssigner
    candidate_topk: 10
    iou_weight: 6
  nms:
    name: MultiClassNMS
    nms_top_k: 1000
    keep_top_k: 100
    score_threshold: 0.025
    nms_threshold: 0.6

# Optimizer
epoch: 100

LearningRate:
  base_lr: 0.4
  schedulers:
  - name: CosineDecay
    max_epochs: 100
  - name: LinearWarmup
    start_factor: 0.1
    steps: 300

OptimizerBuilder:
  optimizer:
    momentum: 0.9
    type: Momentum
  regularizer:
    factor: 0.00004
    type: L2

# Exporting the model
export:
  post_process: True  # Whether post-processing is included in the network when export model.
  nms: True           # Whether NMS is included in the network when export model.
  benchmark: False    # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
  fuse_conv_bn: False