73 lines
1.7 KiB
YAML
73 lines
1.7 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: ${env}_pre_diffusion_unet_ta${horizon_steps}_td${denoising_steps}
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logdir: ${oc.env:DPPO_LOG_DIR}/furniture-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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train_dataset_path: ${oc.env:DPPO_DATA_DIR}/furniture/${task}_${randomness}/train.pkl
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seed: 42
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device: cuda:0
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task: lamp
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randomness: med
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env: ${task}_${randomness}_dim
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obs_dim: 44
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action_dim: 10
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transition_dim: ${action_dim}
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denoising_steps: 100
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horizon_steps: 16
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cond_steps: 1
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wandb:
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entity: ${oc.env:DPPO_WANDB_ENTITY}
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project: furniture-${task}-${randomness}-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: 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: 64
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dim_mults: [1, 2, 4]
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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}'}
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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} |