release
This commit is contained in:
allenzren
@@ -0,0 +1,68 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
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||||
run:
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||||
dir: ${logdir}
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||||
_target_: agent.pretrain.train_diffusion_agent.TrainDiffusionAgent
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||||
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||||
name: ${env}_pre_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
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||||
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env}/train.pkl
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||||
|
||||
seed: 42
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||||
device: cuda:0
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||||
env: can
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obs_dim: 23
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||||
action_dim: 7
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||||
transition_dim: ${action_dim}
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||||
denoising_steps: 20
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||||
horizon_steps: 4
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||||
cond_steps: 1
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||||
|
||||
wandb:
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entity: ${oc.env:DPPO_WANDB_ENTITY}
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project: robomimic-${env}-pretrain
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||||
run: ${now:%H-%M-%S}_${name}
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||||
|
||||
train:
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||||
n_epochs: 8000
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||||
batch_size: 256
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||||
learning_rate: 1e-4
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||||
weight_decay: 1e-6
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||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
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||||
min_lr: 1e-5
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||||
epoch_start_ema: 20
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||||
update_ema_freq: 10
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||||
save_model_freq: 1000
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||||
|
||||
model:
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||||
_target_: model.diffusion.diffusion.DiffusionModel
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||||
predict_epsilon: True
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||||
denoised_clip_value: 1.0
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||||
network:
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||||
_target_: model.diffusion.mlp_diffusion.DiffusionMLP
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||||
time_dim: 16
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||||
mlp_dims: [512, 512, 512]
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||||
residual_style: True
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||||
cond_dim: ${obs_dim}
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||||
horizon_steps: ${horizon_steps}
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||||
transition_dim: ${transition_dim}
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||||
horizon_steps: ${horizon_steps}
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||||
obs_dim: ${obs_dim}
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||||
action_dim: ${action_dim}
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||||
transition_dim: ${transition_dim}
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||||
denoising_steps: ${denoising_steps}
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||||
cond_steps: ${cond_steps}
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||||
device: ${device}
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||||
|
||||
ema:
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||||
decay: 0.995
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||||
|
||||
train_dataset:
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||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
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||||
dataset_path: ${train_dataset_path}
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||||
horizon_steps: ${horizon_steps}
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||||
cond_steps: ${cond_steps}
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||||
device: ${device}
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||||
@@ -0,0 +1,91 @@
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||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_diffusion_agent.TrainDiffusionAgent
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||||
|
||||
name: ${env}_pre_diffusion_mlp_img_ta${horizon_steps}_td${denoising_steps}
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||||
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env}-img/train.pkl
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||||
|
||||
seed: 42
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||||
device: cuda:0
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||||
env: can
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||||
obs_dim: 9 # proprioception only
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||||
action_dim: 7
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||||
transition_dim: ${action_dim}
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||||
denoising_steps: 100
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||||
horizon_steps: 4
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||||
cond_steps: 1
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||||
|
||||
wandb:
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||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
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||||
project: robomimic-${env}-pretrain
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||||
run: ${now:%H-%M-%S}_${name}
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||||
|
||||
shape_meta:
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obs:
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rgb:
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shape: [3, 96, 96]
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state:
|
||||
shape: [9]
|
||||
action:
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shape: [7]
|
||||
|
||||
train:
|
||||
n_epochs: 5000
|
||||
batch_size: 256
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 5000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 500
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||||
|
||||
model:
|
||||
_target_: model.diffusion.diffusion.DiffusionModel
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||||
predict_epsilon: True
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||||
denoised_clip_value: 1.0
|
||||
network:
|
||||
_target_: model.diffusion.mlp_diffusion.VisionDiffusionMLP
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||||
backbone:
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_target_: model.common.vit.VitEncoder
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||||
obs_shape: ${shape_meta.obs.rgb.shape}
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||||
cfg:
|
||||
patch_size: 8
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||||
depth: 1
|
||||
embed_dim: 128
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||||
num_heads: 4
|
||||
embed_style: embed2
|
||||
embed_norm: 0
|
||||
augment: True
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||||
spatial_emb: 128
|
||||
time_dim: 32
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||||
mlp_dims: [512, 512, 512]
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||||
residual_style: True
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
obs_dim: ${obs_dim}
|
||||
action_dim: ${action_dim}
|
||||
transition_dim: ${transition_dim}
|
||||
denoising_steps: ${denoising_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
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||||
|
||||
ema:
|
||||
decay: 0.995
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||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
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||||
use_img: True
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||||
dataset_path: ${train_dataset_path}
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||||
horizon_steps: ${horizon_steps}
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||||
max_n_episodes: 100
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||||
cond_steps: ${cond_steps}
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||||
device: ${device}
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||||
@@ -0,0 +1,71 @@
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||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_diffusion_agent.TrainDiffusionAgent
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||||
|
||||
name: ${env}_pre_diffusion_unet_ta${horizon_steps}_td${denoising_steps}
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||||
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env}/train.pkl
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||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: can
|
||||
obs_dim: 23
|
||||
action_dim: 7
|
||||
transition_dim: ${action_dim}
|
||||
denoising_steps: 20
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: robomimic-${env}-pretrain
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||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 8000
|
||||
batch_size: 256
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||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
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||||
min_lr: 1e-5
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||||
epoch_start_ema: 20
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||||
update_ema_freq: 10
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||||
save_model_freq: 1000
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||||
|
||||
model:
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||||
_target_: model.diffusion.diffusion.DiffusionModel
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||||
predict_epsilon: True
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||||
denoised_clip_value: 1.0
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||||
network:
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||||
_target_: model.diffusion.unet.Unet1D
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diffusion_step_embed_dim: 16
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dim: 40
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||||
dim_mults: [1, 2]
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kernel_size: 5
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||||
n_groups: 8
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||||
smaller_encoder: False
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||||
cond_predict_scale: True
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||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
obs_dim: ${obs_dim}
|
||||
action_dim: ${action_dim}
|
||||
transition_dim: ${transition_dim}
|
||||
denoising_steps: ${denoising_steps}
|
||||
cond_steps: ${cond_steps}
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||||
device: ${device}
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||||
|
||||
ema:
|
||||
decay: 0.995
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||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
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||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
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||||
device: ${device}
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||||
@@ -0,0 +1,60 @@
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defaults:
|
||||
- _self_
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||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: ${env}_pre_gaussian_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env}/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: can
|
||||
obs_dim: 23
|
||||
action_dim: 7
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: robomimic-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 5000
|
||||
batch_size: 256
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 5000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gaussian.GaussianModel
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||||
network:
|
||||
_target_: model.common.mlp_gaussian.Gaussian_MLP
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||||
mlp_dims: [512, 512, 512]
|
||||
residual_style: True
|
||||
fixed_std: 0.1
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,83 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: ${env}_pre_gaussian_mlp_img_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env}-img/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: can
|
||||
obs_dim: 9 # proprioception only
|
||||
action_dim: 7
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: robomimic-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
shape_meta:
|
||||
obs:
|
||||
rgb:
|
||||
shape: [3, 96, 96]
|
||||
state:
|
||||
shape: [9]
|
||||
action:
|
||||
shape: [7]
|
||||
|
||||
train:
|
||||
n_epochs: 1000
|
||||
batch_size: 256
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 5000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 500
|
||||
|
||||
model:
|
||||
_target_: model.common.gaussian.GaussianModel
|
||||
network:
|
||||
_target_: model.common.mlp_gaussian.Gaussian_VisionMLP
|
||||
backbone:
|
||||
_target_: model.common.vit.VitEncoder
|
||||
obs_shape: ${shape_meta.obs.rgb.shape}
|
||||
cfg:
|
||||
patch_size: 8
|
||||
depth: 1
|
||||
embed_dim: 128
|
||||
num_heads: 4
|
||||
embed_style: embed2
|
||||
embed_norm: 0
|
||||
augment: True
|
||||
spatial_emb: 128
|
||||
mlp_dims: [512, 512, 512]
|
||||
residual_style: True
|
||||
fixed_std: 0.1
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
use_img: True
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
max_n_episodes: 100
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,62 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: ${env}_pre_gaussian_transformer_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env}/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: can
|
||||
obs_dim: 23
|
||||
action_dim: 7
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: robomimic-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 5000
|
||||
batch_size: 256
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 5000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gaussian.GaussianModel
|
||||
network:
|
||||
_target_: model.common.transformer.Gaussian_Transformer
|
||||
transformer_embed_dim: 96
|
||||
transformer_num_heads: 4
|
||||
transformer_num_layers: 4
|
||||
fixed_std: 0.1
|
||||
learn_fixed_std: False
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,62 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: ${env}_pre_gmm_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env}/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: can
|
||||
obs_dim: 23
|
||||
action_dim: 7
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
num_modes: 5
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: robomimic-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 5000
|
||||
batch_size: 256
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 5000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gmm.GMMModel
|
||||
network:
|
||||
_target_: model.common.mlp_gmm.GMM_MLP
|
||||
mlp_dims: [512, 512, 512]
|
||||
residual_style: True
|
||||
fixed_std: 0.1
|
||||
num_modes: ${num_modes}
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,64 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: ${env}_pre_gmm_transformer_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env}/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: can
|
||||
obs_dim: 23
|
||||
action_dim: 7
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
num_modes: 5
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: robomimic-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 5000
|
||||
batch_size: 256
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 5000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gmm.GMMModel
|
||||
network:
|
||||
_target_: model.common.transformer.GMM_Transformer
|
||||
transformer_embed_dim: 96
|
||||
transformer_num_heads: 4
|
||||
transformer_num_layers: 4
|
||||
fixed_std: 0.1
|
||||
learn_fixed_std: False
|
||||
num_modes: ${num_modes}
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
Reference in New Issue
Block a user