hrnet_w32_256x256_mpii.yml

use_gpu: true
log_iter: 5
save_dir: output
snapshot_epoch: 10
weights: output/hrnet_w32_256x256_mpii/model_final
epoch: 210
num_joints: &num_joints 16
pixel_std: &pixel_std 200
metric: KeyPointTopDownMPIIEval
num_classes: 1
train_height: &train_height 256
train_width: &train_width 256
trainsize: &trainsize [*train_width, *train_height]
hmsize: &hmsize [64, 64]
flip_perm: &flip_perm [[0, 5], [1, 4], [2, 3], [10, 15], [11, 14], [12, 13]]

#####model
architecture: TopDownHRNet
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/Trunc_HRNet_W32_C_pretrained.pdparams

TopDownHRNet:
  backbone: HRNet
  post_process: HRNetPostProcess
  flip_perm: *flip_perm
  num_joints: *num_joints
  width: &width 32
  loss: KeyPointMSELoss

HRNet:
  width: *width
  freeze_at: -1
  freeze_norm: false
  return_idx: [0]

KeyPointMSELoss:
  use_target_weight: true


#####optimizer
LearningRate:
  base_lr: 0.0005
  schedulers:
  - !PiecewiseDecay
    milestones: [170, 200]
    gamma: 0.1
  - !LinearWarmup
    start_factor: 0.001
    steps: 1000

OptimizerBuilder:
  optimizer:
    type: Adam
  regularizer:
    factor: 0.0
    type: L2


#####data
TrainDataset:
  !KeypointTopDownMPIIDataset
    image_dir: images
    anno_path: annotations/mpii_train.json
    dataset_dir: dataset/mpii
    num_joints: *num_joints


EvalDataset:
  !KeypointTopDownMPIIDataset
    image_dir: images
    anno_path: annotations/mpii_val.json
    dataset_dir: dataset/mpii
    num_joints: *num_joints


TestDataset:
  !ImageFolder
    anno_path: dataset/coco/keypoint_imagelist.txt

worker_num: 4
global_mean: &global_mean [0.485, 0.456, 0.406]
global_std: &global_std [0.229, 0.224, 0.225]
TrainReader:
  sample_transforms:
    - RandomFlipHalfBodyTransform:
        scale: 0.5
        rot: 40
        num_joints_half_body: 8
        prob_half_body: 0.3
        pixel_std: *pixel_std
        trainsize: *trainsize
        upper_body_ids: [7, 8, 9, 10, 11, 12, 13, 14, 15]
        flip_pairs: *flip_perm
    - TopDownAffine:
        trainsize: *trainsize
    - ToHeatmapsTopDown:
        hmsize: *hmsize
        sigma: 2
  batch_transforms:
    - NormalizeImage:
        mean: *global_mean
        std: *global_std
        is_scale: true
    - Permute: {}
  batch_size: 64
  shuffle: true
  drop_last: false

EvalReader:
  sample_transforms:
    - TopDownAffine:
        trainsize: *trainsize
  batch_transforms:
    - NormalizeImage:
        mean: *global_mean
        std: *global_std
        is_scale: true
    - Permute: {}
  batch_size: 16

TestReader:
  inputs_def:
    image_shape: [3, *train_height, *train_width]
  sample_transforms:
    - Decode: {}
    - TopDownEvalAffine:
        trainsize: *trainsize
    - NormalizeImage:
        mean: *global_mean
        std: *global_std
        is_scale: true
    - Permute: {}
  batch_size: 1