# 3D ResNet-RS-50 video classification on Kinetics-400.
#
# --experiment_type=video_classification_kinetics400
# Expected accuracy: 78.2% top-1 accuracy.
runtime:
  mixed_precision_dtype: bfloat16
task:
  losses:
    l2_weight_decay: 0.00004
    label_smoothing: 0.1
    one_hot: true
  model:
    aggregate_endpoints: false
    backbone:
      resnet_3d_rs:
        model_id: 50
        stem_type: 'v1'
        stem_conv_temporal_kernel_size: 5
        stem_conv_temporal_stride: 2
        stem_pool_temporal_stride: 1
        stochastic_depth_drop_rate: 0.1
        se_ratio: 0.25
      type: resnet_3d_rs
    dropout_rate: 0.5
    model_type: video_classification
    norm_activation:
      activation: relu
      norm_epsilon: 1.0e-05
      norm_momentum: 0.0
      use_sync_bn: false
  train_data:
    data_format: channels_last
    drop_remainder: true
    dtype: bfloat16
    feature_shape: !!python/tuple
    - 32
    - 224
    - 224
    - 3
    file_type: sstable
    global_batch_size: 1024
    is_training: true
    min_image_size: 256
    name: kinetics400
    num_channels: 3
    num_classes: 400
    num_examples: 215570
    num_test_clips: 1
    num_test_crops: 1
    one_hot: true
    temporal_stride: 2
    aug_max_area_ratio: 1.0
    aug_max_aspect_ratio: 2.0
    aug_min_area_ratio: 0.08
    aug_min_aspect_ratio: 0.5
  validation_data:
    data_format: channels_last
    drop_remainder: false
    dtype: bfloat16
    feature_shape: !!python/tuple
    - 32
    - 256
    - 256
    - 3
    file_type: sstable
    global_batch_size: 64
    is_training: false
    min_image_size: 256
    name: kinetics400
    num_channels: 3
    num_classes: 400
    num_examples: 17706
    num_test_clips: 10
    num_test_crops: 3
    one_hot: true
    temporal_stride: 2
trainer:
  checkpoint_interval: 210
  max_to_keep: 3
  optimizer_config:
    ema:
      average_decay: 0.9999
      trainable_weights_only: false
    learning_rate:
      cosine:
        decay_steps: 73682
        initial_learning_rate: 0.8
        name: CosineDecay
      type: cosine
    warmup:
      linear:
        name: linear
        warmup_learning_rate: 0
        warmup_steps: 1050
      type: linear
  train_steps: 73682
  steps_per_loop: 500
  summary_interval: 500
  validation_interval: 500
