runtime:
  distribution_strategy: 'tpu'
  mixed_precision_dtype: 'float32'
task:
  model:
    num_classes: 1001
    input_size: [224, 224, 3]
    backbone:
      type: 'mobilenet'
      mobilenet:
        model_id: 'MobileNetV3Large'
        filter_size_scale: 1.0
    dropout_rate: 0.3
  losses:
    l2_weight_decay: 1.0e-06  # 1/10 of original value.
    one_hot: true
    label_smoothing: 0.1
  train_data:
    input_path: '/readahead/200M/placer/prod/home/distbelief/imagenet-tensorflow/imagenet-2012-tfrecord/train*'
    is_training: true
    global_batch_size: 4096
    dtype: 'float32'
    aug_rand_hflip: true
    aug_type:
      autoaug:
        augmentation_name: v0
        cutout_const: 100
        translate_const: 250
      type: autoaug
    drop_remainder: true
  validation_data:
    input_path: '/readahead/200M/placer/prod/home/distbelief/imagenet-tensorflow/imagenet-2012-tfrecord/valid*'
    is_training: false
    global_batch_size: 4096
    dtype: 'float32'
    drop_remainder: false
    aug_rand_hflip: true
  quantization:
    pretrained_original_checkpoint: 'gs://**/mobilenetv3_baseline_31/ckpt-156000'
trainer:
  # With below setting, the accuracy of QAT reaches to accuracy 0.74.43 after ~2 hours with 4x4 DF.
  train_steps: 62400
  validation_steps: 13
  validation_interval: 312
  steps_per_loop: 312
  summary_interval: 312
  checkpoint_interval: 312
  optimizer_config:
    learning_rate:
      cosine:
        alpha: 0.0
        decay_steps: 62400
        initial_learning_rate: 0.0003  # 1/10 of original lr.
        name: CosineDecay
        offset: 0
      type: cosine
    optimizer:
      adamw:
        amsgrad: false
        beta_1: 0.9
        beta_2: 0.999
        epsilon: 1.0e-07
        gradient_clip_norm: 1.0
        weight_decay_rate: 0.0
      type: adamw
    warmup:
      type: 'linear'
      linear:
        warmup_steps: 0
