Add diffusion, vqbet, act bigg
This commit is contained in:
Remi Cadene
@@ -42,7 +42,7 @@ training:
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online_steps_between_rollouts: 1
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delta_timestamps:
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action: "[i / ${fps} for i in range(1, ${policy.chunk_size}+1)]"
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action: "[i / ${fps} + 1 / ${fps} for i in range(${policy.chunk_size})]"
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eval:
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n_episodes: 50
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102
lerobot/configs/policy/act_koch_real_bigger.yaml
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102
lerobot/configs/policy/act_koch_real_bigger.yaml
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# @package _global_
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# Use `act_koch_real.yaml` to train on real-world datasets collected on Alexander Koch's robots.
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# Compared to `act.yaml`, it contains 2 cameras (i.e. laptop, phone) instead of 1 camera (i.e. top).
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# Also, `training.eval_freq` is set to -1. This config is used to evaluate checkpoints at a certain frequency of training steps.
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# When it is set to -1, it deactivates evaluation. This is because real-world evaluation is done through our `control_robot.py` script.
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# Look at the documentation in header of `control_robot.py` for more information on how to collect data , train and evaluate a policy.
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#
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# Example of usage for training:
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# ```bash
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# python lerobot/scripts/train.py \
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# policy=act_koch_real \
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# env=koch_real
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# ```
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seed: 1000
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dataset_repo_id: lerobot/koch_pick_place_lego
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override_dataset_stats:
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observation.images.laptop:
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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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observation.images.phone:
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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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training:
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offline_steps: 40000
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online_steps: 0
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eval_freq: -1
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save_freq: 5000
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log_freq: 10
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save_checkpoint: true
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batch_size: 64
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lr: 1e-4
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lr_backbone: 1e-4
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weight_decay: 1e-4
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grad_clip_norm: 10
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online_steps_between_rollouts: 1
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delta_timestamps:
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action: "[i / ${fps} + 1 / ${fps} for i in range(${policy.chunk_size})]"
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eval:
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n_episodes: 50
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batch_size: 50
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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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# Input / output structure.
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n_obs_steps: 1
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chunk_size: 100
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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.laptop: [3, 480, 640]
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observation.images.phone: [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.laptop: mean_std
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observation.images.phone: 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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dim_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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# Note: Although the original ACT implementation has 7 for `n_decoder_layers`, there is a bug in the code
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# that means only the first layer is used. Here we match the original implementation by setting this to 1.
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# See this issue https://github.com/tonyzhaozh/act/issues/25#issue-2258740521.
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n_decoder_layers: 8
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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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temporal_ensemble_momentum: null
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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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102
lerobot/configs/policy/diffusion_koch_real.yaml
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102
lerobot/configs/policy/diffusion_koch_real.yaml
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@@ -0,0 +1,102 @@
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# @package _global_
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# Defaults for training for the PushT dataset as per https://github.com/real-stanford/diffusion_policy.
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# Note: We do not track EMA model weights as we discovered it does not improve the results. See
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# https://github.com/huggingface/lerobot/pull/134 for more details.
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seed: 100000
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dataset_repo_id: lerobot/koch_pick_place_lego
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override_dataset_stats:
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observation.images.laptop:
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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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observation.images.phone:
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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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training:
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offline_steps: 40000
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online_steps: 0
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eval_freq: -1
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save_freq: 5000
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log_freq: 100
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save_checkpoint: true
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batch_size: 64
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grad_clip_norm: 10
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lr: 1.0e-4
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lr_scheduler: cosine
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lr_warmup_steps: 500
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adam_betas: [0.95, 0.999]
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adam_eps: 1.0e-8
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adam_weight_decay: 1.0e-6
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online_steps_between_rollouts: 1
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delta_timestamps:
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observation.images.cam_right_wrist: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
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observation.images.cam_left_wrist: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
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observation.images.cam_high: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
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observation.images.cam_low: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
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observation.state: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
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action: "[i / ${fps} + 1 / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1 - ${policy.n_obs_steps} + ${policy.horizon})]"
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eval:
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n_episodes: 50
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batch_size: 50
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policy:
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name: diffusion
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# Input / output structure.
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n_obs_steps: 1
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horizon: 100
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n_action_steps: 100
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input_shapes:
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# TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
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observation.images.laptop: [3, 480, 640]
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observation.images.phone: [3, 480, 640]
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observation.state: ["${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.laptop: mean_std
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observation.images.phone: 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 / modeling.
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# Vision backbone.
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vision_backbone: resnet18
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crop_shape: null
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crop_is_random: False
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pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
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use_group_norm: False
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spatial_softmax_num_keypoints: 512
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# Unet.
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down_dims: [512, 1024, 2048]
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kernel_size: 5
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n_groups: 8
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diffusion_step_embed_dim: 128
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use_film_scale_modulation: True
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# Noise scheduler.
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noise_scheduler_type: DDPM
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num_train_timesteps: 100
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beta_schedule: squaredcos_cap_v2
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beta_start: 0.0001
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beta_end: 0.02
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prediction_type: epsilon # epsilon / sample
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clip_sample: True
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clip_sample_range: 1.0
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# Inference
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num_inference_steps: 100
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# Loss computation
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do_mask_loss_for_padding: false
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@@ -16,17 +16,17 @@ override_dataset_stats:
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std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
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training:
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offline_steps: 80000
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offline_steps: 40000
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online_steps: 0
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eval_freq: -1
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save_freq: 10000
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save_freq: 5000
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save_checkpoint: true
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batch_size: 8
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batch_size: 64
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grad_clip_norm: 10
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lr: 1.0e-4
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lr_scheduler: cosine
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lr_warmup_steps: 2000
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lr_warmup_steps: 500
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adam_betas: [0.95, 0.999]
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adam_eps: 1.0e-8
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adam_weight_decay: 1.0e-6
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@@ -34,7 +34,7 @@ training:
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# VQ-BeT specific
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vqvae_lr: 1.0e-3
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n_vqvae_training_steps: 20000
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n_vqvae_training_steps: 500
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bet_weight_decay: 2e-4
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bet_learning_rate: 5.5e-5
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bet_betas: [0.9, 0.999]
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@@ -43,7 +43,7 @@ training:
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observation.images.laptop: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
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observation.images.phone: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
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observation.state: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
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action: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, ${policy.n_action_pred_token} + ${policy.action_chunk_size} - 1)]"
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action: "[i / ${fps} + 1 / ${fps} for i in range(1 - ${policy.n_obs_steps}, ${policy.n_action_pred_token} + ${policy.action_chunk_size} - 1)]"
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eval:
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n_episodes: 50
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@@ -53,9 +53,9 @@ policy:
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name: vqbet
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# Input / output structure.
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n_obs_steps: 5
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n_obs_steps: 1
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n_action_pred_token: 7
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action_chunk_size: 5
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action_chunk_size: 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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