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seed: 0
output_dir: output/04_04_2024_00_18_28_1061204
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
log_dir: output/04_04_2024_00_18_28_1061204
debug_distributed: false
wb_tag: default
wb_cont_run: 37fe2kep
log_interval: 10
script_name: run_resnet_30dataset_traj1000_embed_128_batch1024_800k_cons
save_wb_checkpoint: true
slurm_job_id: '25682608'
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: 32
debug_submitit: false
ngpus: 8
nodes: 4
timeout: 4320
job_dir: logs/
partition: learnlab
use_volta32: true
comment: ''
resume: logs/
dist_url: file:///checkpoint/xinleic/experiments/b96f1c1c8a1e49f691901ccebe7e3f1f_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
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: 1000
step_cnt: 10000000
data_augmentation: false
use_disk: true
pad_before: 0
data_ratio: 1
action_horizon: 8
observation_horizon: 4
dataset_postfix: _traj1000
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: 128
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
stem:
modalities:
- image
- state
modality_embed_dim: 128
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
image:
_target_: hpt_pretrain.models.policy_stem.MLP
input_dim: 512
output_dim: 128
widths:
- 128
num_of_copy: 1
state:
_target_: hpt_pretrain.models.policy_stem.MLP
input_dim: 7
output_dim: 128
widths:
- 128
head:
_target_: hpt_pretrain.models.policy_head.MLP
input_dim: 128
tanh_end: true
output_dim: 56
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: 2
ddp_val_dataloader:
num_workers: 8
pin_memory: false
persistent_workers: false
drop_last: false
prefetch_factor: 2
optimizer:
_target_: torch.optim.AdamW
lr: 0.0008
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: 3000
total_iters: 800000
epoch_iters: 1000
validation_iters: 100
use_accumulation: false
pretrained_dir: ''
max_validation_size: 10
accumulate_batch_step: 1
lr_scheduler:
_target_: torch.optim.lr_scheduler.ConstantLR
factor: 1
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