train.yaml

AUTO_RESUME: False
DATA_DIR: './data/imagenet'
MODEL: 'Supernet_Training'
RESUME_PATH: './experiments/workspace/train/resume.pth.tar'
SAVE_PATH: './'
SEED: 42
LOG_INTERVAL: 50
RECOVERY_INTERVAL: 0
WORKERS: 8
NUM_GPU: 8
SAVE_IMAGES: False
AMP: False
OUTPUT: 'None'
EVAL_METRICS: 'prec1'
TTA: 0
LOCAL_RANK: 0

DATASET:
  NUM_CLASSES: 1000
  IMAGE_SIZE: 224 # image patch size
  INTERPOLATION: 'bilinear' # Image resize interpolation type
  BATCH_SIZE: 128 # batch size

NET:
  GP: 'avg'
  DROPOUT_RATE: 0.0

  EMA:
    USE: True
    FORCE_CPU: False # force model ema to be tracked on CPU
    DECAY: 0.9998

OPT: 'sgd'
LR: 1.0
EPOCHS: 120
META_LR: 1e-4

BATCHNORM:
  SYNC_BN: False

SUPERNET:
  UPDATE_ITER: 200
  SLICE: 4
  POOL_SIZE: 10
  RESUNIT: False
  DIL_CONV: False
  UPDATE_2ND: True
  FLOPS_MINIMUM: 0
  FLOPS_MAXIMUM: 600
  PICK_METHOD: 'meta'
  META_STA_EPOCH: 20
  HOW_TO_PROB: 'pre_prob'
  PRE_PROB: (0.05,0.2,0.05,0.5,0.05,0.15)