cifar_best.yaml

DESC:
  Example CIFAR sweep 3 of 3 (trains the best model from cifar_regnet sweep).
  Train the best RegNet-125M from cifar_regnet sweep for variable epoch lengths.
  Trains 3 copies of every model (to obtain mean and std of the error).
  The purpose of this sweep is to show how to train FINAL version of a model.
NAME: cifar/cifar_best
SETUP:
  # Number of configs to sample
  NUM_CONFIGS: 12
  # SAMPLERS for optimization parameters
  SAMPLERS:
    OPTIM.MAX_EPOCH:
      TYPE: value_sampler
      VALUES: [50, 100, 200, 400]
    RNG_SEED:
      TYPE: int_sampler
      RAND_TYPE: uniform
      RANGE: [1, 3]
      QUANTIZE: 1
  CONSTRAINTS:
    REGNET:
      NUM_STAGES: [2, 2]
  # BASE_CFG is RegNet-125MF (best model from cifar_regnet sweep)
  BASE_CFG:
    MODEL:
      TYPE: regnet
      NUM_CLASSES: 10
    REGNET:
      STEM_TYPE: res_stem_cifar
      SE_ON: True
      STEM_W: 16
      DEPTH: 12
      W0: 96
      WA: 19.5
      WM: 2.942
      GROUP_W: 8
    OPTIM:
      BASE_LR: 1.0
      LR_POLICY: cos
      MAX_EPOCH: 50
      MOMENTUM: 0.9
      NESTEROV: True
      WARMUP_EPOCHS: 5
      WEIGHT_DECAY: 0.0005
      EMA_ALPHA: 0.00025
      EMA_UPDATE_PERIOD: 32
    BN:
      USE_CUSTOM_WEIGHT_DECAY: True
    TRAIN:
      DATASET: cifar10
      SPLIT: train
      BATCH_SIZE: 1024
      IM_SIZE: 32
      MIXED_PRECISION: True
      LABEL_SMOOTHING: 0.1
      MIXUP_ALPHA: 0.5
    TEST:
      DATASET: cifar10
      SPLIT: test
      BATCH_SIZE: 1000
      IM_SIZE: 32
    NUM_GPUS: 1
    DATA_LOADER:
      NUM_WORKERS: 4
    LOG_PERIOD: 25
    VERBOSE: False
# Launch config options
LAUNCH:
  PARTITION: devlab
  NUM_GPUS: 1
  PARALLEL_JOBS: 12
  TIME_LIMIT: 180
# Analyze config options
ANALYZE:
  PLOT_METRIC_VALUES: False
  PLOT_COMPLEXITY_VALUES: False
  PLOT_CURVES_BEST: 3
  PLOT_CURVES_WORST: 0
  PLOT_MODELS_BEST: 1
  METRICS: []
  COMPLEXITY: [flops, params, acts, memory, epoch_fw_bw, epoch_time]
  PRE_FILTERS: {done: [0, 1, 1]}
  SPLIT_FILTERS:
    epochs=050: {cfg.OPTIM.MAX_EPOCH: [ 50,  50,  50]}
    epochs=100: {cfg.OPTIM.MAX_EPOCH: [100, 100, 100]}
    epochs=200: {cfg.OPTIM.MAX_EPOCH: [200, 200, 200]}
    epochs=400: {cfg.OPTIM.MAX_EPOCH: [400, 400, 400]}