architecture_config:
  model: MlpMixer_ext_1
  model_params:
    input_n: 10
    joints: 22
    output_n: 25
    n_txcnn_layers: 4
    txc_kernel_size: 3
    reduction: 8
    hidden_dim: 64
    input_gcn:
      model_complexity:
        - 64
        - 64
        - 64
        - 64
      interpretable:
        - true
        - true
        - true
        - true
        - true
    output_gcn:
      model_complexity:
        - 3
      interpretable:
        - true
    clipping: 15
learning_config:
  WarmUp: 100
  normalize: false
  dropout: 0.1
  weight_decay: 1e-4
  epochs: 50
  lr: 0.01
#  max_norm: 3
  scheduler:
    type: StepLR
    params:
      step_size: 3000
      gamma: 0.8
  loss:
    weights: ""
    type: "mpjpe"
  augmentations:
    random_scale:
      x:
        - 0.95
        - 1.05
      y:
        - 0.90
        - 1.10
      z:
        - 0.95
        - 1.05
    random_noise: ""
    random_flip:
      x: true
      y: ""
      z: true
    random_rotation:
      x:
        - -5
        - 5
      y:
        - -180
        - 180
      z:
        - -5
        - 5
    random_translation:
      x:
        - -0.10
        - 0.10
      y:
        - -0.10
        - 0.10
      z:
        - -0.10
        - 0.10
environment_config:
  actions: all
  evaluate_from: 0
  is_norm: true
  job: 16
  sample_rate: 2
  return_all_joints: true
  save_grads: false
  test_batch: 128
  train_batch: 128
general_config:
  data_dir: /ai-research/datasets/attention/ann_h3.6m/
  experiment_name: STSGCN-tests
  load_model_path: ''
  log_path: /ai-research/notebooks/testing_repos/logdir/
  model_name_rel_path: STSGCN-benchmark
  save_all_intermediate_models: false
  save_models: true
  tensorboard:
    num_mesh: 4
meta_config:
  comment: Testing a new architecture based on STSGCN paper.
  project: Attention
  task: 3d keypoint prediction
  version: 0.1.1