runtime:
  distribution_strategy: 'mirrored'
  mixed_precision_dtype: 'float32'
  loss_scale: 'dynamic'
task:
  model:
    num_classes: 1001
    input_size: [224, 224, 3]
    backbone:
      type: 'resnet'
      resnet:
        model_id: 50
  losses:
    l2_weight_decay: 0
    one_hot: true
    label_smoothing: 0.1
  train_data:
    input_path: 'gs://mlcompass-data/imagenet/imagenet-2012-tfrecord/train*'
    is_training: true
    global_batch_size: 1024
    dtype: 'float32'
  validation_data:
    input_path: 'gs://mlcompass-data/imagenet/imagenet-2012-tfrecord/valid*'
    is_training: false
    global_batch_size: 1024
    dtype: 'float32'
    drop_remainder: false
  pruning:
    pretrained_original_checkpoint: 'gs://**/resnet_classifier_gpu/ckpt-56160'
    pruning_schedule: 'PolynomialDecay'
    begin_step: 0
    end_step: 40000
    initial_sparsity: 0.2
    final_sparsity: 0.8
    frequency: 40
trainer:
  # Top1 accuracy 75.508% after 7hr for 8 GPUs with pruning.
  # Pretrained network without pruning has Top1 accuracy 76.7%
  train_steps: 50000
  validation_steps: 50
  validation_interval: 1251
  steps_per_loop: 1251
  summary_interval: 1251
  checkpoint_interval: 1251
  optimizer_config:
    optimizer:
      type: 'sgd'
      sgd:
        momentum: 0.9
    learning_rate:
      type: 'exponential'
      exponential:
        initial_learning_rate: 0.01
        decay_steps: 2502
        decay_rate: 0.9
        staircase: true
    warmup:
      type: 'linear'
      linear:
        warmup_steps: 0
