e1ef4ca1cf
* move ema update within pretraining epoch * update pretraining ema configs * add lift and can epoch 8000 checkpoint url * add note about EMA issue in pretraining instruction
65 lines
1.5 KiB
YAML
65 lines
1.5 KiB
YAML
defaults:
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- _self_
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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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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.npz
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seed: 42
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device: cuda:0
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env: transport
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obs_dim: 59
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action_dim: 14
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denoising_steps: 20
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horizon_steps: 8
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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:
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first_cycle_steps: 10000
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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: 1
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save_model_freq: 500
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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: 32
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mlp_dims: [1024, 1024, 1024]
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residual_style: True
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cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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horizon_steps: ${horizon_steps}
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action_dim: ${action_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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denoising_steps: ${denoising_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.StitchedSequenceDataset
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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} |