Datasets:

arjundd

/

meddlr-data

like 0

USE_SUPERVISED_CONSISTENCY: False

CONSISTENCY:

AUG:

MOTION:

RANGE: (0.2, 0.5)

SCHEDULER:

WARMUP_ITERS: 0

WARMUP_METHOD:

MRI_RECON:

AUG_SENSITIVITY_MAPS: True

SCHEDULER_P:

IGNORE: False

TRANSFORMS: ()

NOISE:

MASK:

RHO: 1.0

SCHEDULER:

WARMUP_ITERS: 0

WARMUP_METHOD:

STD_DEV: (1,)

LATENT_LOSS_NAME: mag_l1

LATENT_LOSS_WEIGHT: 0.1

LOSS_NAME: l1

LOSS_WEIGHT: 0.1

NUM_LATENT_LAYERS: 1

USE_CONSISTENCY: True

USE_LATENT: 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

M2R:

META_ARCHITECTURE: GeneralizedUnrolledCNN

USE_SUPERVISED_CONSISTENCY: False

META_ARCHITECTURE: GeneralizedUnrolledCNN

N2R:

META_ARCHITECTURE: GeneralizedUnrolledCNN

USE_SUPERVISED_CONSISTENCY: False

NM2R:

META_ARCHITECTURE: GeneralizedUnrolledCNN

USE_SUPERVISED_CONSISTENCY: False

NORMALIZER:

KEYWORDS: ()

NAME: TopMagnitudeNormalizer

RECON_LOSS:

NAME: l1

RENORMALIZE_DATA: True

SEG:

ACTIVATION: sigmoid

CLASSES: ()

INCLUDE_BACKGROUND: False

SSDU:

MASKER:

PARAMS:

META_ARCHITECTURE: GeneralizedUnrolledCNN

UNET:

BLOCK_ORDER: ('conv', 'relu', 'conv', 'relu', 'batchnorm', 'dropout')

CHANNELS: 32

DROPOUT: 0.0

IN_CHANNELS: 2

NORMALIZE: False

NUM_POOL_LAYERS: 4

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: 128

NUM_RESBLOCKS: 2

NUM_UNROLLED_STEPS: 8

PADDING:

SHARE_WEIGHTS: False

WEIGHTS:

OUTPUT_DIR: /bmrNAS/people/arjun/results/meddlr/tests/basic

SEED: -1

SOLVER:

BASE_LR: 0.0001

BIAS_LR_FACTOR: 1.0

CHECKPOINT_PERIOD: 200

GAMMA: 0.1

GRAD_ACCUM_ITERS: 1

LR_SCHEDULER_NAME: WarmupMultiStepLR

MAX_ITER: 1600

MOMENTUM: 0.9

OPTIMIZER: Adam

STEPS: (30000,)