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s1lent4gnt
83b2dc1219
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
62 lines
1.6 KiB
YAML
62 lines
1.6 KiB
YAML
# @package _global_
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defaults:
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- _self_
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hydra:
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run:
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# Set `dir` to where you would like to save all of the run outputs. If you run another training session
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# with the same value for `dir` its contents will be overwritten unless you set `resume` to true.
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dir: outputs/train_hilserl_classifier/${now:%Y-%m-%d}/${now:%H-%M-%S}_${env.name}_${hydra.job.name}
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job:
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name: default
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seed: 13
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dataset_repo_id: aractingi/push_cube_square_light_reward_cropped_resized
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# aractingi/push_cube_square_reward_1_cropped_resized
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dataset_root: data/aractingi/push_cube_square_light_reward_cropped_resized
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local_files_only: true
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train_split_proportion: 0.8
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# Required by logger
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env:
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name: "classifier"
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task: "binary_classification"
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training:
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num_epochs: 6
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batch_size: 16
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learning_rate: 1e-4
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num_workers: 4
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grad_clip_norm: 10
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use_amp: true
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log_freq: 1
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eval_freq: 1 # How often to run validation (in epochs)
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save_freq: 1 # How often to save checkpoints (in epochs)
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save_checkpoint: true
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image_keys: ["observation.images.front", "observation.images.side"]
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label_key: "next.reward"
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profile_inference_time: false
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profile_inference_time_iters: 20
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eval:
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batch_size: 16
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num_samples_to_log: 30 # Number of validation samples to log in the table
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policy:
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name: "hilserl/classifier"
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model_name: "helper2424/resnet10" # "facebook/convnext-base-224
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model_type: "cnn"
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num_cameras: 2 # Has to be len(training.image_keys)
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wandb:
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enable: false
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project: "classifier-training"
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job_name: "classifier_training_0"
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disable_artifact: false
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device: "mps"
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resume: false
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output_dir: "outputs/classifier/old_trainer_resnet10_frozen"
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