# 3D ResNet-50 video classification on Kinetics-600.
#
# --experiment_type=video_classification_kinetics600
# Expected accuracy: 79.5% top-1, 94.8% top-5.
runtime:
  distribution_strategy: 'tpu'
  mixed_precision_dtype: 'bfloat16'
task:
  model:
    dropout_rate: 0.5
    norm_activation:
      use_sync_bn: false
    backbone:
      resnet_3d:
        block_specs: !!python/tuple
        - temporal_kernel_sizes: !!python/tuple
          - 1
          - 1
          - 1
          temporal_strides: 1
          use_self_gating: false
        - temporal_kernel_sizes: !!python/tuple
          - 1
          - 1
          - 1
          - 1
          temporal_strides: 1
          use_self_gating: false
        - temporal_kernel_sizes: !!python/tuple
          - 3
          - 3
          - 3
          - 3
          - 3
          - 3
          temporal_strides: 1
          use_self_gating: false
        - temporal_kernel_sizes: !!python/tuple
          - 3
          - 3
          - 3
          temporal_strides: 1
          use_self_gating: false
        model_id: 50
        stem_conv_temporal_kernel_size: 5
        stem_conv_temporal_stride: 2
        stem_pool_temporal_stride: 1
  train_data:
    name: kinetics600
    feature_shape: !!python/tuple
    - 32
    - 224
    - 224
    - 3
    temporal_stride: 2
    global_batch_size: 1024
    dtype: 'bfloat16'
    shuffle_buffer_size: 1024
    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:
    name: kinetics600
    feature_shape: !!python/tuple
    - 32
    - 256
    - 256
    - 3
    temporal_stride: 2
    num_test_clips: 10
    num_test_crops: 3
    global_batch_size: 64
    dtype: 'bfloat16'
    drop_remainder: false
trainer:
  optimizer_config:
    learning_rate:
      cosine:
        initial_learning_rate: 0.8
        decay_steps: 71488
    warmup:
      linear:
        warmup_steps: 1787
  train_steps: 71488
  steps_per_loop: 500
  summary_interval: 500
  validation_interval: 500
