mask_rcnn_r50_fpn_1x_qat.yml

pretrain_weights: https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_1x_coco.pdparams
slim: QAT

QAT:
  quant_config: {
    'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max',
    'weight_bits': 8, 'activation_bits': 8, 'dtype': 'int8', 'window_size': 10000, 'moving_rate': 0.9,
    'quantizable_layer_type': ['Conv2D', 'Linear']}
  print_model: True

epoch: 5

TrainReader:
  batch_size: 1

LearningRate:
  base_lr: 0.001
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [3, 4]
  - !LinearWarmup
    start_factor: 0.001
    steps: 100