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

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# Subject Independent (SI) evaluation configuration
# Usage: python EEG_Encoder/run_CLIPtraining.py --config-name=eval_si

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: "zscore_global"
    zscore_epsilon: 1.0e-8

experiment:
  models: [ATMSmodify]
  datasets: ['thingEEG','TUAB','ImageNetEEG','TUEV','SEED',]
  gpu_number: ['0']  # Evaluation typically uses single GPU

modes:
  only_evaluate_SD: false
  only_evaluate_SI: true
  save_model: false


advanced:
  model_checkpoint_name: ${paths.CHECKPOINT_DIR}/ALL/ATMSmodify_original.pth
features:
  use_shm: true
  auto_clean_shm_when_exit: false

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

# Example usage:
# python EEG_Encoder/run_CLIPtraining.py --config-name=eval_si \
#   advanced.model_checkpoint_name=/path/to/model.pth \
#   experiment.datasets=[ImageNetEEG]