# --experiment_type=retinanet_spinenet_mobile_coco_qat
runtime:
  distribution_strategy: 'mirrored'
  mixed_precision_dtype: 'float32'
task:
  losses:
    l2_weight_decay: 3.0e-05
  model:
    anchor:
      anchor_size: 3
      aspect_ratios: [0.5, 1.0, 2.0]
      num_scales: 3
    backbone:
      spinenet_mobile:
        stochastic_depth_drop_rate: 0.2
        model_id: '49'
        se_ratio: 0.2
        use_keras_upsampling_2d: true
      type: 'spinenet_mobile'
    decoder:
      type: 'identity'
    head:
      num_convs: 4
      num_filters: 48
      use_separable_conv: true
    input_size: [384, 384, 3]
    max_level: 7
    min_level: 3
    norm_activation:
      activation: 'swish'
      norm_epsilon: 0.001
      norm_momentum: 0.99
      use_sync_bn: true
  train_data:
    dtype: 'float32'
    global_batch_size: 128
    is_training: true
    parser:
      aug_rand_hflip: true
      aug_scale_max: 2.0
      aug_scale_min: 0.5
  validation_data:
    dtype: 'float32'
    global_batch_size: 8
    is_training: false
  quantization:
    pretrained_original_checkpoint: 'gs://**/coco_spinenet49_mobile_tpu/ckpt-277200'
trainer:
  checkpoint_interval: 924
  optimizer_config:
    learning_rate:
      stepwise:
        boundaries: [531300, 545160]
        values: [0.0016, 0.00016, 0.000016]
      type: 'stepwise'
    warmup:
      linear:
        warmup_learning_rate: 0.0000335
        warmup_steps: 4000
  steps_per_loop: 924
  train_steps: 554400
  validation_interval: 924
  validation_steps: 1250
  summary_interval: 924
