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hpt-large-lang
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seed: 0
output_dir: output/05_05_2024_20_21_54_727273
domains: austin_sailor_dataset_converted_externally_to_rlds, stanford_hydra_dataset_converted_externally_to_rlds,
  austin_buds_dataset_converted_externally_to_rlds, austin_sirius_dataset_converted_externally_to_rlds,
  berkeley_mvp_converted_externally_to_rlds, berkeley_rpt_converted_externally_to_rlds,
  iamlab_cmu_pickup_insert_converted_externally_to_rlds, utaustin_mutex, imperialcollege_sawyer_wrist_cam,
  stanford_mask_vit_converted_externally_to_rlds, language_table, kuka, bc_z, robo_net,
  dlr_sara_pour_converted_externally_to_rlds, stanford_robocook_converted_externally_to_rlds,
  cmu_play_fusion, bridge, furniture_bench_dataset_converted_externally_to_rlds, ucsd_pick_and_place_dataset_converted_externally_to_rlds,
  usc_cloth_sim_converted_externally_to_rlds, stanford_kuka_multimodal_dataset_converted_externally_to_rlds,
  roboturk, kaist_nonprehensile_converted_externally_to_rlds, asu_table_top_converted_externally_to_rlds,
  utokyo_xarm_pick_and_place_converted_externally_to_rlds, berkeley_cable_routing,
  droid, uiuc_d3field, robo_set, qut_dexterous_manpulation, nyu_door_opening_surprising_effectiveness,
  nyu_franka_play_dataset_converted_externally_to_rlds, mimic_play, maniskill_dataset_converted_externally_to_rlds,
  columbia_cairlab_pusht_real, conq_hose_manipulation, dlr_edan_shared_control_converted_externally_to_rlds,
  berkeley_gnm_sac_son, berkeley_autolab_ur5, aloha_mobile, agent_aware_affordances
log_dir: output/05_05_2024_20_21_54_727273
debug_distributed: false
wb_tag: default
wb_cont_run: 2nxmlp81
log_interval: 10
script_name: run_resnet_30dataset_traj100000_embed_256_batch2048_800k_language
save_wb_checkpoint: true
slurm_job_id: '27155760'
effective_total_epochs: 100
effective_batch_size: 256
epoch_size: 10
total_num_traj: 0
total_num_sample: 0
rank: 0
gpu: 0
task_per_gpu: 1
world_size: 64
debug_submitit: false
ngpus: 8
nodes: 8
timeout: 4320
job_dir: logs/
partition: learnlab
use_volta32: true
comment: ''
resume: logs/
dist_url: file:///checkpoint/xinleic/experiments/43124ff352a546a8b51d5fb4234b51d8_init
dist_on_itp: false
local_rank: 1
distributed: true
dist_backend: nccl
dset_w_temperature: 2.0
dataset_shuffle: true
dataset_groups: ''
nodelist: learnlab,learnfair,scavenge
fsdp: false
count_flops: false
accumuate_gradient_steps: 1
trunk_acc_gradient_step: 1
submitit_autoresume: true
customized_dataset_mixture_file: ''
trunk_separate_opt: false
dataset:
  _target_: hpt_pretrain.dataset.traj_dataset.TrajDataset
  horizon: 5
  val_ratio: 0.1
  pad_after: 0
  precompute_feat: true
  image_encoder: resnet
  episode_cnt: 100000
  step_cnt: 10000000
  data_augmentation: false
  use_disk: true
  pad_before: 0
  data_ratio: 1
  action_horizon: 8
  observation_horizon: 4
  dataset_postfix: _traj100000
  dataset_encoder_postfix: _resnet
  use_multiview: false
  normalize_state: true
  use_heldout_dataset: true
  heldout_dataset: false
  regenerate: false
  continue_generate: false
network:
  _target_: hpt_pretrain.models.policy.Policy
  embed_dim: 256
  num_blocks: 16
  num_heads: 8
  use_modality_embedding: true
  use_domain_embedding: false
  token_postprocessing: mean
  weight_init_style: pytorch
  drop_path: 0.1
  mae_loss_scale: 0.0
  masked_autoencoding: false
  action_horizon: ${dataset.action_horizon}
stem:
  modalities:
  - image
  - state
  - language
  modality_embed_dim: 256
  normalize_state: ${dataset.normalize_state}
  state_embedding_dim: 1
  image_encoder: ${dataset.image_encoder}
  crossattn_dim_head: 64
  crossattn_heads: 8
  crossattn_modality_dropout: 0.1
  observation_horizon: ${dataset.observation_horizon}
  random_horizon_masking: true
  add_pos_embedding_to_state: false
  num_blocks: 1
  crossattn_latent:
    image: 16
    state: 16
    language: 8
  image:
    _target_: hpt_pretrain.models.policy_stem.MLP
    input_dim: 512
    output_dim: 256
    widths:
    - 128
    num_of_copy: 1
  state:
    _target_: hpt_pretrain.models.policy_stem.MLP
    input_dim: 8
    output_dim: 256
    widths:
    - 128
  language:
    _target_: hpt_pretrain.models.policy_stem.MLP
    input_dim: 768
    output_dim: ${network.embed_dim}
    widths:
    - 128
head:
  _target_: hpt_pretrain.models.policy_head.MLP
  input_dim: 256
  tanh_end: true
  output_dim: 48
  dropout: true
  widths:
  - 256
  - 128
dataloader:
  batch_size: 32
  num_workers: 1
  pin_memory: false
  persistent_workers: false
  drop_last: true
val_dataloader:
  num_workers: 1
  pin_memory: false
  persistent_workers: false
ddp_dataloader:
  num_workers: 8
  pin_memory: false
  persistent_workers: false
  drop_last: false
  prefetch_factor: 6
ddp_val_dataloader:
  num_workers: 8
  pin_memory: false
  persistent_workers: false
  drop_last: false
  prefetch_factor: 2
optimizer:
  _target_: torch.optim.AdamW
  lr: 0.001
  eps: 1.0e-06
  weight_decay: 0.05
optimizer_misc:
  nontrunk_lr_scale: 0.5
warmup_lr:
  lr: 1.0e-10
  step: 1000
train:
  total_epochs: 30000
  total_iters: 800000
  epoch_iters: 1000
  validation_iters: 100
  use_accumulation: false
  pretrained_dir: '05_05_2024_20_21_54_727273'
  max_validation_size: 10
  load_pretrain_trunk_only: false
  freeze_trunk: false
lr_scheduler:
  _target_: torch.optim.lr_scheduler.CosineAnnealingLR
  T_max: 800000
  eta_min: 1.0e-06