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Tidy up yaml configs (#121)

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This commit is contained in:
Alexander Soare
2024-04-30 16:08:59 +01:00
committed by GitHub
parent e4e739f4f8
commit 9d60dce6f3
21 changed files with 142 additions and 207 deletions
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36
lerobot/configs/default.yaml
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@@ -9,31 +9,23 @@ hydra:
job:
name: default
seed: 1337
# batch size for TorchRL SerialEnv. Each underlying env will get the seed = seed + env_index
# NOTE: only diffusion policy supports rollout_batch_size > 1
rollout_batch_size: 1
device: cuda # cpu
prefetch: 4
eval_freq: ???
save_freq: ???
eval_episodes: ???
save_video: false
save_model: false
save_buffer: false
train_steps: ???
fps: ???
seed: ???
dataset_repo_id: lerobot/pusht
offline_prioritized_sampler: true
training:
offline_steps: ???
online_steps: ???
online_steps_between_rollouts: ???
eval_freq: ???
save_freq: ???
log_freq: 250
save_model: false
dataset:
repo_id: ???
n_action_steps: ???
n_obs_steps: ???
env: ???
policy: ???
eval:
n_episodes: 1
# TODO(alexander-soare): Right now this does not work. Reinstate this.
batch_size: 1
wandb:
enable: true

11
lerobot/configs/env/aloha.yaml vendored
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@@ -1,18 +1,7 @@
# @package _global_
eval_episodes: 50
eval_freq: 7500
save_freq: 75000
log_freq: 250
# TODO: same as xarm, need to adjust
offline_steps: 25000
online_steps: 25000
fps: 50
dataset:
repo_id: lerobot/aloha_sim_insertion_human
env:
name: aloha
task: AlohaInsertion-v0

11
lerobot/configs/env/pusht.yaml vendored
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@@ -1,18 +1,7 @@
# @package _global_
eval_episodes: 50
eval_freq: 7500
save_freq: 75000
log_freq: 250
# TODO: same as xarm, need to adjust
offline_steps: 25000
online_steps: 25000
fps: 10
dataset:
repo_id: lerobot/pusht
env:
name: pusht
task: PushT-v0

10
lerobot/configs/env/xarm.yaml vendored
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@@ -1,17 +1,7 @@
# @package _global_
eval_episodes: 20
eval_freq: 1000
save_freq: 10000
log_freq: 50
offline_steps: 25000
online_steps: 25000
fps: 15
dataset:
repo_id: lerobot/xarm_lift_medium
env:
name: xarm
task: XarmLift-v0

53
lerobot/configs/policy/act.yaml
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@@ -1,21 +1,34 @@
# @package _global_
offline_steps: 80000
online_steps: 0
seed: 1000
dataset_repo_id: lerobot/aloha_sim_insertion_human
eval_episodes: 1
eval_freq: 10000
save_freq: 100000
log_freq: 250
training:
offline_steps: 80000
online_steps: 0
eval_freq: 10000
save_freq: 100000
log_freq: 250
save_model: true
n_obs_steps: 1
# when temporal_agg=False, n_action_steps=horizon
batch_size: 8
lr: 1e-5
lr_backbone: 1e-5
weight_decay: 1e-4
grad_clip_norm: 10
online_steps_between_rollouts: 1
override_dataset_stats:
observation.images.top:
# stats from imagenet, since we use a pretrained vision model
mean: [[[0.485]], [[0.456]], [[0.406]]] # (c,1,1)
std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
override_dataset_stats:
observation.images.top:
# stats from imagenet, since we use a pretrained vision model
mean: [[[0.485]], [[0.456]], [[0.406]]] # (c,1,1)
std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
delta_timestamps:
action: "[i / ${fps} for i in range(${policy.chunk_size})]"
eval:
n_episodes:: 50
# See `configuration_act.py` for more details.
policy:
@@ -24,7 +37,7 @@ policy:
pretrained_model_path:
# Input / output structure.
n_obs_steps: ${n_obs_steps}
n_obs_steps: 1
chunk_size: 100 # chunk_size
n_action_steps: 100
@@ -66,15 +79,3 @@ policy:
# Training and loss computation.
dropout: 0.1
kl_weight: 10.0
# ---
# TODO(alexander-soare): Remove these from the policy config.
batch_size: 8
lr: 1e-5
lr_backbone: 1e-5
weight_decay: 1e-4
grad_clip_norm: 10
utd: 1
delta_timestamps:
action: "[i / ${fps} for i in range(${policy.chunk_size})]"

57
lerobot/configs/policy/diffusion.yaml
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@@ -1,22 +1,33 @@
# @package _global_
seed: 100000
horizon: 16
n_obs_steps: 2
n_action_steps: 8
dataset_obs_steps: ${n_obs_steps}
past_action_visible: False
keypoint_visible_rate: 1.0
dataset_repo_id: lerobot/pusht
eval_episodes: 50
eval_freq: 5000
save_freq: 5000
log_freq: 250
training:
offline_steps: 200000
online_steps: 0
eval_freq: 5000
save_freq: 5000
log_freq: 250
save_model: true
offline_steps: 200000
online_steps: 0
batch_size: 64
grad_clip_norm: 10
lr: 1.0e-4
lr_scheduler: cosine
lr_warmup_steps: 500
adam_betas: [0.95, 0.999]
adam_eps: 1.0e-8
adam_weight_decay: 1.0e-6
online_steps_between_rollouts: 1
offline_prioritized_sampler: true
delta_timestamps:
observation.image: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
observation.state: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
action: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1 - ${policy.n_obs_steps} + ${policy.horizon})]"
eval:
n_episodes: 50
override_dataset_stats:
# TODO(rcadene, alexander-soare): should we remove image stats as well? do we use a pretrained vision model?
@@ -38,9 +49,9 @@ policy:
pretrained_model_path:
# Input / output structure.
n_obs_steps: ${n_obs_steps}
horizon: ${horizon}
n_action_steps: ${n_action_steps}
n_obs_steps: 2
horizon: 16
n_action_steps: 8
input_shapes:
# TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
@@ -84,23 +95,9 @@ policy:
# ---
# TODO(alexander-soare): Remove these from the policy config.
batch_size: 64
grad_clip_norm: 10
lr: 1.0e-4
lr_scheduler: cosine
lr_warmup_steps: 500
adam_betas: [0.95, 0.999]
adam_eps: 1.0e-8
adam_weight_decay: 1.0e-6
utd: 1
use_ema: true
ema_update_after_step: 0
ema_min_alpha: 0.0
ema_max_alpha: 0.9999
ema_inv_gamma: 1.0
ema_power: 0.75
delta_timestamps:
observation.image: "[i / ${fps} for i in range(1 - ${n_obs_steps}, 1)]"
observation.state: "[i / ${fps} for i in range(1 - ${n_obs_steps}, 1)]"
action: "[i / ${fps} for i in range(1 - ${n_obs_steps}, 1 - ${n_obs_steps} + ${policy.horizon})]"

2
lerobot/configs/policy/tdmpc.yaml
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@@ -54,7 +54,7 @@ policy:
seed_steps: 0
update_freq: 2
tau: 0.01
utd: 1
online_steps_between_rollouts: 1
# offline rl
# dataset_dir: ???