791506dfb8
- Replace `use_pretrained_backbone` with `pretrained_backbone_weights` - Bump diffusers' minimum version `0.26.3` -> `0.27.2` - Add ignore flags in CI's pytest - Change Box observation spaces in simulation environments - Set `version_base="1.2"` in Hydra initializations - Bump einops' minimum version `0.7.0` -> `0.8.0`
81 lines
1.8 KiB
YAML
81 lines
1.8 KiB
YAML
# @package _global_
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offline_steps: 80000
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online_steps: 0
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eval_episodes: 1
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eval_freq: 10000
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save_freq: 100000
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log_freq: 250
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n_obs_steps: 1
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# when temporal_agg=False, n_action_steps=horizon
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override_dataset_stats:
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observation.images.top:
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# stats from imagenet, since we use a pretrained vision model
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mean: [[[0.485]], [[0.456]], [[0.406]]] # (c,1,1)
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std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
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# See `configuration_act.py` for more details.
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policy:
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name: act
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pretrained_model_path:
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# Input / output structure.
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n_obs_steps: ${n_obs_steps}
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chunk_size: 100 # chunk_size
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n_action_steps: 100
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input_shapes:
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# TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
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observation.images.top: [3, 480, 640]
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observation.state: ["${env.state_dim}"]
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output_shapes:
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action: ["${env.action_dim}"]
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# Normalization / Unnormalization
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input_normalization_modes:
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observation.images.top: mean_std
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observation.state: mean_std
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output_normalization_modes:
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action: mean_std
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# Architecture.
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# Vision backbone.
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vision_backbone: resnet18
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pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
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replace_final_stride_with_dilation: false
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# Transformer layers.
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pre_norm: false
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d_model: 512
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n_heads: 8
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dim_feedforward: 3200
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feedforward_activation: relu
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n_encoder_layers: 4
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n_decoder_layers: 1
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# VAE.
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use_vae: true
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latent_dim: 32
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n_vae_encoder_layers: 4
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# Inference.
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use_temporal_aggregation: false
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# Training and loss computation.
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dropout: 0.1
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kl_weight: 10.0
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# ---
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# TODO(alexander-soare): Remove these from the policy config.
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batch_size: 8
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lr: 1e-5
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lr_backbone: 1e-5
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weight_decay: 1e-4
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grad_clip_norm: 10
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utd: 1
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delta_timestamps:
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action: "[i / ${fps} for i in range(${policy.chunk_size})]"
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