callbacks:
  rollout_lh:
    _target_: mode.rollout.libero_rollout.RolloutLibero
    _recursive_: false
    env_cfg:
      _target_: mode.wrappers.hulc_wrapper.HulcWrapper
    skip_epochs: ${rollout_lh_skip_epochs}
    benchmark_name: ${libero_benchmark}
    rollout_freq: 10
    num_videos: 0
    num_sequences: 50
    max_steps: 600
    empty_cache: false
    debug: false
    n_eval: 20
    num_procs: 10
    use_mp: false
    task_embedding_format: clip
    device: ${device}
  checkpoint:
    _target_: pytorch_lightning.callbacks.ModelCheckpoint
    save_top_k: 1
    verbose: true
    monitor: eval_lh/avg_seq_len
    mode: max
    dirpath: saved_models
    filename: '{epoch:02d}_{eval_lh/avg_seq_len:.2f}'
    every_n_epochs: ${callbacks.rollout_lh.rollout_freq}
  ema:
    _target_: mode.callbacks.ema.EMA
    decay: 0.999
    start_step: 0
    save_ema_weights_in_callback_state: true
    evaluate_ema_weights_instead: true
    power: 0.6666666666666666
    inv_gamma: 1.0
    min_value: 0.0
    max_value: 0.9999
datamodule:
  datasets:
    lang_dataset:
      _target_: mode.datasets.libero_dataset.LiberoMultitaskDataset
      key: lang
      benchmark_name: ${libero_benchmark}
      batch_size: ${batch_size}
      proprio_state: ${datamodule.proprioception_dims}
      obs_space: ${datamodule.observation_space}
      num_workers: ${num_workers}
      action_seq_len: ${act_seq_len}
      obs_seq_len: ${obs_seq_len}
      split_ratio: 0.0
  transforms:
    train:
      rgb_static:
      - _target_: torchvision.transforms.Resize
        size: 224
        antialias: true
      - _target_: mode.utils.transforms.RandomShiftsAug
        pad: 10
      - _target_: mode.utils.transforms.ScaleImageTensor
      - _target_: torchvision.transforms.Normalize
        mean:
        - 0.48145466
        - 0.4578275
        - 0.40821073
        std:
        - 0.26862954
        - 0.26130258
        - 0.27577711
      rgb_gripper:
      - _target_: torchvision.transforms.Resize
        size: 112
        antialias: true
      - _target_: mode.utils.transforms.RandomShiftsAug
        pad: 4
      - _target_: mode.utils.transforms.ScaleImageTensor
      - _target_: torchvision.transforms.Normalize
        mean:
        - 0.48145466
        - 0.4578275
        - 0.40821073
        std:
        - 0.26862954
        - 0.26130258
        - 0.27577711
    val:
      rgb_static:
      - _target_: torchvision.transforms.Resize
        size: 224
        antialias: true
      - _target_: mode.utils.transforms.ScaleImageTensor
      - _target_: torchvision.transforms.Normalize
        mean:
        - 0.48145466
        - 0.4578275
        - 0.40821073
        std:
        - 0.26862954
        - 0.26130258
        - 0.27577711
      rgb_gripper:
      - _target_: torchvision.transforms.Resize
        size: 112
        antialias: true
      - _target_: mode.utils.transforms.ScaleImageTensor
      - _target_: torchvision.transforms.Normalize
        mean:
        - 0.48145466
        - 0.4578275
        - 0.40821073
        std:
        - 0.26862954
        - 0.26130258
        - 0.27577711
  _target_: mode.datasets.libero_data_module.LiberoDataModule
  _recursive_: false
  root_data_dir: ${root_data_dir}
  action_space: 7
  shuffle_val: false
  benchmark_name: ${libero_benchmark}
  observation_space:
    rgb_obs:
    - agentview_rgb
    - eye_in_hand_rgb
    depth_obs: []
    state_obs:
    - gripper_states
    - joint_states
    actions:
    - rel_actions
    language:
    - language
  proprioception_dims: None
model:
  language_goal:
    _target_: mode.models.networks.clip_lang_encoder.LangClip
    _recursive_: false
    model_name: ${clip_lang_model_name}
  model:
    _target_: mode.models.edm_diffusion.score_wrappers.GCDenoiser
    _recursive_: false
    sigma_data: ${model.sigma_data}
    inner_model:
      _target_: mode.models.networks.modedit.MoDeDiT
      action_dim: ${datamodule.action_space}
      goal_dim: ${model.cond_dim}
      obs_dim: ${obs_dim}
      goal_conditioned: true
      causal: true
      use_custom_attn_mask: false
      use_proprio: ${model.use_proprio}
      state_dim: ${proprio_dims}
      embed_dim: ${model.latent_dim}
      n_layers: 12
      goal_seq_len: 1
      obs_seq_len: ${obs_seq_len}
      action_seq_len: ${act_seq_len}
      embed_pdrob: 0
      goal_drop: 0.1
      attn_pdrop: 0.3
      mlp_pdrop: 0.1
      n_heads: 8
      device: ${device}
      linear_output: true
      cond_router: true
      num_experts: 4
      top_k: 2
      router_normalize: true
      use_goal_in_routing: false
      use_argmax: false
      use_shared_expert: false
      use_noise_token_as_input: true
      init_style: olmoe
  _target_: mode.models.mode_agent.MoDEAgent
  _recursive_: false
  multistep: ${multistep}
  use_lr_scheduler: true
  entropy_gamma: 0.01
  router_z_delta: 0.0
  use_proprio: false
  seed: ${seed}
  sampler_type: ddim
  num_sampling_steps: 5
  sigma_data: 0.5
  sigma_min: 0.001
  sigma_max: 80
  noise_scheduler: exponential
  sigma_sample_density_type: loglogistic
  ckpt_path: /home/reuss/code/MeDiT_Policy/convert_weights/mode_first_run
  start_from_pretrained: true
  act_window_size: ${act_seq_len}
  latent_dim: 1024
  obs_enc_dim: ${obs_dim}
  cond_dim: 512
  resnet_type: '50'
  optimizer:
    _target_: torch.optim.AdamW
    transformer_weight_decay: 0.05
    obs_encoder_weight_decay: 0.05
    learning_rate: 0.0001
    betas:
    - 0.9
    - 0.95
  lr_scheduler:
    lr_scheduler:
      init_lr: 0.0001
      init_lr_scale: 0.1
      final_lr_scale: 1.0e-06
      total_steps: 45000
      phase_ratio: (0.02, 0.08, 0.9)
      lr: 0.0001
root_data_dir: /home/yagmurlu/code/MoDE_Calvin/dataset/task_ABC_D
lang_folder: lang_clip_resnet50
vis_clip_model_name: ViT-B/16
clip_lang_model_name: ViT-B/32
log_dir: ./logs
slurm: false
future_range: 29
seed: 242
device: cuda
batch_size: 128
devices: 2
goal_window_size: 1
act_dim: 7
proprio_dims: 9
obs_dim: 512
goal_dim: 512
obs_seq_len: 1
act_seq_len: 10
multistep: ${act_seq_len}
p_last_state: 0
gen_img_res: 112
max_epochs: 10
rollout_lh_skip_epochs: 9
num_workers: 1
benchmark_name: ${libero_benchmark}
libero_benchmark: libero_90
trainer:
  gpus: ${devices}
  precision: 32
  max_epochs: ${max_epochs}
  sync_batchnorm: false
  accelerator: auto
  limit_train_batches: 1000
  limit_val_batches: 4
logger:
  _target_: pytorch_lightning.loggers.WandbLogger
  save_dir: .
  name: logger
  group: mode
  log_model: false
  project: ${libero_benchmark}
  entity: bennoq
  id: ???