WaveMind/EEG_Encoder/examples/train_atms.yaml at master · FreedomIntelligence/WaveMind · GitHub

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# Quick start configuration for ATMSmodify training
# Usage: python EEG_Encoder/run_CLIPtraining.py --config-name=train_atms

defaults:
  - base
  - _self_

# ============================================================================
# DATA AUGMENTATION
# ============================================================================
augmentation:
  train:
    enable: false
    normalization_method: "random"
    zscore_epsilon: 1.0e-8
    amplitude_fluctuation_enable: true
    amplitude_fluctuation_range: [0.9, 1.1]
  test:
    enable: false
    normalization_method: "std_global"
    zscore_epsilon: 1.0e-8

experiment:
  models: ['ATMSmodify']
  # datasets: ['all']
  datasets: ['thingEEG','TUAB','ImageNetEEG','TUEV','SEED']
  # datasets: ['ImageNetEEG']
  gpu_number: ['all']
  # limit_sample: 1e5

training:
  DEFAULT_EPOCHS: 30
  DEFAULT_BATCH_SIZE: 2048
  DEFAULT_NUM_WORKERS: 64
  DEFAULT_LEARNING_RATE: 1e-3
  ACCUMULATE_GRAD_BATCHES: 2

modes:
  save_model: true

paths:
  HDF5_PATH: ${paths.DATA_DIR}/data_label_original.h5

features:
  use_shm: true
  auto_clean_shm_when_exit: false
  use_dynamic_sampling: true


logger:
  type: "swanlab"
  # type: "none"

advanced:
  model_checkpoint_name: ${paths.CHECKPOINT_DIR}/ALL/ATMSmodify_original.pth