AUG_TEST:
  UNDERSAMPLE:
    ACCELERATIONS:
    - 6
AUG_TRAIN:
  NOISE_P: 0.2
  UNDERSAMPLE:
    ACCELERATIONS:
    - 6
    CALIBRATION_SIZE: 24
    CENTER_FRACTIONS: []
    NAME: PoissonDiskMaskFunc
    PRECOMPUTE:
      NUM: -1
      SEED: -1
  USE_NOISE: false
CUDNN_BENCHMARK: false
DATALOADER:
  ALT_SAMPLER:
    PERIOD_SUPERVISED: 1
    PERIOD_UNSUPERVISED: 1
  DATA_KEYS: []
  DROP_LAST: true
  FILTER:
    BY: []
  GROUP_SAMPLER:
    AS_BATCH_SAMPLER: true
    BATCH_BY:
    - inplane_shape
  NUM_WORKERS: 8
  PREFETCH_FACTOR: 2
  SAMPLER_TRAIN: ''
  SUBSAMPLE_TRAIN:
    NUM_TOTAL: -1
    NUM_TOTAL_BY_GROUP: []
    NUM_UNDERSAMPLED: 0
    NUM_VAL: -1
    NUM_VAL_BY_GROUP: []
    SEED: 1000
DATASETS:
  QDESS:
    DATASET_TYPE: qDESSImageDataset
    ECHO_KIND: echo1-echo2-mc
    KWARGS:
    - orientation
    - sagittal
  TEST:
  - stanford_qdess_v0.1.0_test
  TRAIN:
  - stanford_qdess_v0.1.0_train
  VAL:
  - stanford_qdess_v0.1.0_val
DESCRIPTION:
  BRIEF: UNet segmentation following parameters used in MedSegPy - input=echo1-echo2-mc,
    100 epochs, 0.001 lr w/ 0.9x decay every (2,) epochs, early stopping- T=12, delta=1e-05,
    bsz=16, qdess args=('orientation', 'sagittal')
  ENTITY_NAME: ss_recon
  EXP_NAME: seg-baseline/unet-medsegpy-echo1-echo2-mc
  PROJECT_NAME: ss_recon
  TAGS:
  - seg-baseline
  - baseline
  - unet-medsegpy
  - neurips
MODEL:
  CASCADE:
    ITFS:
      PERIOD: 0
    RECON_MODEL_NAME: ''
    SEG_MODEL_NAME: ''
    SEG_NORMALIZE: ''
    USE_MAGNITUDE: false
    ZERO_FILL: false
  CS:
    MAX_ITER: 200
    REGULARIZATION: 0.005
  DENOISING:
    META_ARCHITECTURE: GeneralizedUnrolledCNN
    NOISE:
      STD_DEV:
      - 1
      USE_FULLY_SAMPLED_TARGET: true
      USE_FULLY_SAMPLED_TARGET_EVAL: null
  DEVICE: cpu
  META_ARCHITECTURE: GeneralizedUNet
  N2R:
    META_ARCHITECTURE: GeneralizedUnrolledCNN
    USE_SUPERVISED_CONSISTENCY: false
  NORMALIZER:
    KEYWORDS: []
    NAME: TopMagnitudeNormalizer
  PARAMETERS:
    INIT:
    - initializers: (("kaiming_normal_", {"nonlinearity":"relu"}), "zeros_")
      kind: conv
      patterns: (".*weight", ".*bias")
    - initializers: ("ones_", "zeros_")
      kind: norm
      patterns: (".*weight", ".*bias")
    - initializers: ("xavier_uniform_",)
      patterns: ("output_block\.weight",)
    USE_COMPLEX_WEIGHTS: false
  RECON_LOSS:
    NAME: l1
    RENORMALIZE_DATA: true
    WEIGHT: 1.0
  SEG:
    ACTIVATION: sigmoid
    CLASSES:
    - pc
    - fc
    - men
    - tc
    INCLUDE_BACKGROUND: false
    IN_CHANNELS: 2
    LOSS_NAME: FlattenedDiceLoss
    LOSS_WEIGHT: 1.0
    MODEL:
      DYNUNET_MONAI:
        DEEP_SUPERVISION: false
        DEEP_SUPR_NUM: 1
        KERNEL_SIZE:
        - 3
        NORM_NAME: instance
        RES_BLOCK: false
        STRIDES:
        - 1
        UPSAMPLE_KERNEL_SIZE:
        - 2
      UNET_MONAI:
        ACTIVATION:
        - prelu
        - {}
        CHANNELS: []
        DROPOUT: 0.0
        KERNEL_SIZE:
        - 3
        NORM:
        - instance
        - {}
        NUM_RES_UNITS: 0
        STRIDES: []
        UP_KERNEL_SIZE:
        - 3
      VNET_MONAI:
        ACTIVATION:
        - elu
        - inplace: true
        DROPOUT_DIM: 3
        DROPOUT_PROB: 0.5
    USE_MAGNITUDE: true
  TASKS:
  - sem_seg
  TB_RECON:
    CHANNELS:
    - 16
    - 32
    - 64
    DEC_NUM_CONV_BLOCKS:
    - 2
    - 3
    ENC_NUM_CONV_BLOCKS:
    - 1
    - 2
    - 3
    KERNEL_SIZE:
    - 5
    MULTI_CONCAT: []
    ORDER:
    - conv
    - relu
    STRIDES:
    - 2
    USE_MAGNITUDE: false
  UNET:
    BLOCK_ORDER:
    - conv
    - relu
    - conv
    - relu
    - bn
    CHANNELS: 32
    DROPOUT: 0.0
    IN_CHANNELS: 2
    NUM_POOL_LAYERS: 5
    OUT_CHANNELS: 2
  UNROLLED:
    CONV_BLOCK:
      ACTIVATION: relu
      NORM: none
      NORM_AFFINE: false
      ORDER:
      - norm
      - act
      - drop
      - conv
    DROPOUT: 0.0
    FIX_STEP_SIZE: false
    KERNEL_SIZE:
    - 3
    NUM_EMAPS: 1
    NUM_FEATURES: 256
    NUM_RESBLOCKS: 2
    NUM_UNROLLED_STEPS: 5
    PADDING: ''
    SHARE_WEIGHTS: false
  WEIGHTS: ''
OUTPUT_DIR: results://skm-tea/neurips2021/U-Net_E1-oplus-E2
SEED: 9001
SOLVER:
  BASE_LR: 0.001
  CHECKPOINT_MONITOR: val_loss
  CHECKPOINT_PERIOD: 1
  EARLY_STOPPING:
    MIN_DELTA: 1.0e-05
    MONITOR: val_loss
    PATIENCE: 12
  GAMMA: 0.9
  GRAD_ACCUM_ITERS: 1
  LR_SCHEDULER_NAME: StepLR
  MAX_ITER: 100
  MIN_LR: 1.0e-08
  MOMENTUM: 0.9
  OPTIMIZER: Adam
  STEPS:
  - 2
  TEST_BATCH_SIZE: 40
  TRAIN_BATCH_SIZE: 16
  WARMUP_FACTOR: 0.001
  WARMUP_ITERS: 1000
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.0
  WEIGHT_DECAY_NORM: 0.0
TEST:
  EVAL_PERIOD: 1
  EXPECTED_RESULTS: []
  FLUSH_PERIOD: -5
  QDESS_EVALUATOR:
    ADDITIONAL_PATHS: []
  VAL_METRICS:
    RECON: []
    SEM_SEG:
    - DSC
    - VOE
    - CV
    - DSC_scan
    - VOE_scan
    - CV_scan
TIME_SCALE: epoch
VERSION: 1
VIS_PERIOD: -100