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# Config for U-Net as implemented in MedSegPy.
# Implementation based on:
#   - Desai et al. Technical considerations for semantic segmentation in MRI. ArXiv 2019.
#   - Desai et al. "Deep learning for medical image segmentation". MICCAI 2018.
MODEL:
  TASKS: ("sem_seg",)
  PARAMETERS:
    INIT:
    - 
      kind: "conv"
      patterns: (".*weight", ".*bias")
      initializers: (("kaiming_normal_", {"nonlinearity":"relu"}), "zeros_")
    - 
      kind: "norm"
      patterns: (".*weight", ".*bias")
      initializers: ("ones_", "zeros_")
    -
      patterns: ("output_block\.weight",)
      initializers: ("xavier_uniform_",)
  META_ARCHITECTURE: "GeneralizedUNet"
  UNET:
    CHANNELS: 32
    NUM_POOL_LAYERS: 5
    DROPOUT: 0.0
    BLOCK_ORDER: ("conv", "relu", "conv", "relu", "bn")
  SEG:
    LOSS_NAME: "FlattenedDiceLoss"
    CLASSES: ("pc", "fc", "men", "tc")
DATASETS:
  TRAIN: ("skmtea_v1_train",)
  VAL: ("skmtea_v1_val",)
  TEST: ("skmtea_v1_test",)
  QDESS:
    ECHO_KIND: "echo1"  # This must be specified - one of ("echo1", "echo2", "echo1-echo2-mc", "rss")
    DATASET_TYPE: "qDESSImageDataset"
    KWARGS: ("orientation", "sagittal")
DATALOADER:
  NUM_WORKERS: 8
  GROUP_SAMPLER:
    BATCH_BY: ("inplane_shape",)
    AS_BATCH_SAMPLER: True
SOLVER:
  OPTIMIZER: "Adam"
  LR_SCHEDULER_NAME: "StepLR"
  GAMMA: 0.9
  STEPS: (2,)  # drop by 0.9x every 2 epochs
  BASE_LR: 1e-3
  MIN_LR: 1e-8
  TRAIN_BATCH_SIZE: 16
  TEST_BATCH_SIZE: 16
  MAX_ITER: 100
  WEIGHT_DECAY: 0.
  CHECKPOINT_PERIOD: 1
  EARLY_STOPPING:
    MONITOR: "val_loss"
    PATIENCE: 12
    MIN_DELTA: 1e-5
DESCRIPTION:
  BRIEF: f"UNet segmentation following parameters used in MedSegPy - input={DATASETS.QDESS.ECHO_KIND}, {SOLVER.MAX_ITER} epochs, {SOLVER.BASE_LR} lr w/ {SOLVER.GAMMA}x decay every {SOLVER.STEPS} epochs, early stopping- T={SOLVER.EARLY_STOPPING.PATIENCE}, delta={SOLVER.EARLY_STOPPING.MIN_DELTA}, bsz={SOLVER.TRAIN_BATCH_SIZE}, qdess args={DATASETS.QDESS.KWARGS}"
  PROJECT_NAME: "skm-tea"
  ENTITY_NAME: "billzhonggz"
  EXP_NAME: f"seg-baseline/unet-medsegpy-{DATASETS.QDESS.ECHO_KIND}-seed={SEED}"
  TAGS: ("seg-baseline", "baseline", "unet-medsegpy", "neurips")
TEST:
  EVAL_PERIOD: 1
  VAL_METRICS:
    SEM_SEG: ("DSC","VOE","CV","DSC_scan","VOE_scan","CV_scan")
  FLUSH_PERIOD: -5
VIS_PERIOD: -100
TIME_SCALE: "epoch"
OUTPUT_DIR: f"results://skm-tea/seg-baseline/unet-{DATASETS.QDESS.ECHO_KIND}-seed={SEED}"
SEED: 9001
VERSION: 1

AUG_TEST:
  UNDERSAMPLE:
    ACCELERATIONS: (6,)
AUG_TRAIN:
  NOISE_P: 0.2
  UNDERSAMPLE:
    ACCELERATIONS: (6,)
    CALIBRATION_SIZE: 24
    CENTER_FRACTIONS: ()
    MAX_ATTEMPTS: 5
    NAME: PoissonDiskMaskFunc
    PRECOMPUTE:
      NUM: -1
      SEED: -1
      USE_MULTIPROCESSING: False
  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
    KWARGS: ('orientation', 'sagittal')
  TEST: ('skmtea_v1_test',)
  TRAIN: ('skmtea_v1_train',)
  VAL: ('skmtea_v1_val',)
DESCRIPTION:
  BRIEF: UNet segmentation following parameters used in MedSegPy - input=echo1, 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: billzhonggz
  EXP_NAME: seg-baseline/unet-medsegpy-echo1-seed=9001
  PROJECT_NAME: skm-tea
  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: None
  DEVICE: cuda
  META_ARCHITECTURE: GeneralizedUNet
  N2R:
    META_ARCHITECTURE: GeneralizedUnrolledCNN
    USE_SUPERVISED_CONSISTENCY: False
  NORMALIZER:
    KEYWORDS: ()
    NAME: TopMagnitudeNormalizer
  PARAMETERS:
    INIT: ({'kind': 'conv', 'patterns': '(".*weight", ".*bias")', 'initializers': '(("kaiming_normal_", {"nonlinearity":"relu"}), "zeros_")'}, {'kind': 'norm', 'patterns': '(".*weight", ".*bias")', 'initializers': '("ones_", "zeros_")'}, {'patterns': '("output_block\\.weight",)', 'initializers': '("xavier_uniform_",)'})
    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: None
    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: 2
        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
    NORMALIZE: False
    NUM_POOL_LAYERS: 5
    OUT_CHANNELS: 2
  UNROLLED:
    BLOCK_ARCHITECTURE: ResNet
    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
    STEP_SIZES: (-2.0,)
  WEIGHTS: 
OUTPUT_DIR: ./results/skm-tea/seg-baseline/unet-echo1-seed=9001/version_001
SEED: 9001
SOLVER:
  BASE_LR: 0.001
  BIAS_LR_FACTOR: 1.0
  CHECKPOINT_MONITOR: val_loss
  CHECKPOINT_PERIOD: 1
  EARLY_STOPPING:
    MIN_DELTA: 1e-05
    MONITOR: val_loss
    PATIENCE: 12
  GAMMA: 0.9
  GRAD_ACCUM_ITERS: 1
  LR_SCHEDULER_NAME: StepLR
  MAX_ITER: 100
  MIN_LR: 1e-08
  MOMENTUM: 0.9
  OPTIMIZER: Adam
  STEPS: (2,)
  TEST_BATCH_SIZE: 16
  TRAIN_BATCH_SIZE: 16
  WARMUP_FACTOR: 0.001
  WARMUP_ITERS: 1000
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.0
  WEIGHT_DECAY_BIAS: 0.0001
  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