File size: 1,581 Bytes
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shape_meta: &shape_meta
# acceptable types: rgb, low_dim
obs:
high_images:
shape: [3, 480, 640]
type: rgb
wrist_images:
shape: [3, 480, 640]
type: rgb
states:
shape: [13]
action:
shape: [13]
task_name: &task_name aloha
dataset_type: &dataset_type mh
dataset_path: &dataset_path data/aloha/placing_drawer_demo/placing_drawer_demo.hdf5
# abs_action: &abs_action True
env_runner:
_target_: diffusion_policy.env_runner.robomimic_image_runner.RobomimicImageRunner
dataset_path: *dataset_path
shape_meta: *shape_meta
n_train: 6
n_train_vis: 0
train_start_idx: 0
n_test: 50
n_test_vis: 0
test_start_seed: 100000
# use python's eval function as resolver, single-quoted string as argument
max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'}
n_obs_steps: ${n_obs_steps}
n_action_steps: ${n_action_steps}
render_obs_key: 'agentview_image'
fps: 10
crf: 22
past_action: ${past_action_visible}
# abs_action: *abs_action
tqdm_interval_sec: 1.0
n_envs: 25
# evaluation at this config requires a 16 core 64GB instance.
dataset:
_target_: diffusion_policy.dataset.aloha_replay_image_dataset.AlohaReplayImageDataset
shape_meta: *shape_meta
dataset_path: *dataset_path
horizon: ${horizon}
pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'}
pad_after: ${eval:'${n_action_steps}-1'}
n_obs_steps: ${dataset_obs_steps}
# abs_action: *abs_action
# rotation_rep: 'rotation_6d'
# use_legacy_normalizer: False
use_cache: True
seed: 42
val_ratio: 0.02
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