unet_quant.yaml

Global:
  model_dir: ./Unet_cityscapes_without_argmax_infer/
  format: paddle
  model_filename: model.pdmodel
  params_filename: model.pdiparams
  qat_image_path: ./train_stuttgart
  ptq_image_path: ./train_stuttgart
  input_list: ['x']
  qat_preprocess: ppseg_cityscapes_qat_preprocess
  ptq_preprocess: ppseg_cityscapes_ptq_preprocess
  qat_batch_size: 2


Distillation:
  alpha: 1.0
  loss: l2
  node:
  - conv2d_37.tmp_1

QuantAware:
  onnx_format: True
  quantize_op_types:
  - conv2d
  - depthwise_conv2d


TrainConfig:
  epochs: 10
  eval_iter: 180
  learning_rate: 0.0005
  optimizer_builder:
    optimizer:
      type: SGD
    weight_decay: 4.0e-05

PTQ:
  calibration_method: 'avg'   # option: avg, abs_max, hist, KL, mse
  skip_tensor_list: None