config.yaml

# @package _global_

# specify here default training configuration
defaults:
  - _self_
  - trainer: default.yaml
  - model: milli_modus_prime_vi-transformer16.yaml
  - datamodule: tali.yaml
  - callbacks: wandb.yaml
  - logger: wandb.yaml # set logger here or use command line (e.g. `python run.py logger=wandb`)

  # modes are special collections of config options for different purposes, e.g. debugging
  - mode: default.yaml

  # enable color logging
  - hydra: logging.yaml



resume: True
checkpoint_path: null

dataloading_only_run: False
visualize_data_locally: False

wandb_visualization_config:
  visualize_data_in_wandb: False
  visualize_preds_in_wandb: False
  upload_interval_in_num_samples: 100
  num_samples_to_upload_per_set: 10000
  sets_to_upload:
    - train
    - val
    - test

# pretty print config at the start of the run using Rich library
print_config: True

# disable python warnings if they annoy you
ignore_warnings: True
logging_level: INFO
# evaluate on test set, using best model weights achieved during training
# lightning chooses best weights based on metric specified in checkpoint callback
test_after_training: True

batch_size: null
# seed for random number generators in pytorch, numpy and python.random
seed : 0

wandb_project_name: ${env:WANDB_PROJECT}
# name of the run, should be used along with experiment mode
name: ${datamodule.dataset_config.dataset_name}-${model.name}-${datamodule.dataset_config.modality_config.name}-${seed}

# path to original working directory
# hydra hijacks working directory by changing it to the new log directory
# so it's useful to have this path as a special variable
# https://hydra.cc/docs/next/tutorials/basic/running_your_app/working_directory
code_dir: ${hydra:runtime.cwd}
root_experiment_dir: ${env:EXPERIMENTS_DIR}
current_experiment_dir: ${root_experiment_dir}/${name}/
# path to folder with data
train_data_dir: ${env:TRAIN_DATASET_DIR}
val_data_dir: ${env:VAL_DATASET_DIR}
test_data_dir: ${env:TEST_DATASET_DIR}