train.yaml

defaults:
    - encoder: temporal_ssl
    - learner: temporal_ssl
    - dataset: temporal_joint_diff_dataset
    - optimizer: adam  #  tactile_curved_ssl

seed: 42
device: cuda

data_representations: ['image','allegro', 'franka'] # for tactile training we don't need images
learner_type: temporal_ssl # Can be bet, bc_gmm, image_byol, tactile_stacked_byol # tactile_stacked_byol tactile_linear_byol, bc, tactile_byol
self_supervised: false

# Hyperparameters to be used everywhere

batch_size: 64
tactile_image_size: 224 # 224 # This could be changed for stacked or shared architectures
vision_image_size: 480
vision_view_num: 0
train_epochs: 1000
save_frequency: 10
train_dset_split: 0.95

distributed: true
num_workers: 4
world_size: 1
num_gpus: 4

# Data path to be set
# experiment: ${learner_type}_bs_${batch_size}_epochs_${train_epochs}_view_${vision_view_num}_${object}_frame_diff_${dataset.frame_diff}_resnet # Name of the experiment that the models are saved
experiment: small_box_open
data_dir: /data/small_box_final/
checkpoint_dir: ??? # Will be set to hydra dir inside the code

# logger
logger: true # To init logger or not
log_frequency: 1

# hydra configuration - should be received separately
hydra:
    run:
        dir: /home/aadhithya/Workspace/tactile-learning/franka_allegro/out/${now:%Y.%m.%d}/${now:%H-%M}_${experiment}