Datasets:

arjundd

/

meddlr-data

like 0

TEST: ("mridata_knee_2019_test",)

DATALOADER:

NUM_WORKERS: 0 # for debugging purposes

SOLVER:

TRAIN_BATCH_SIZE: 1

TEST_BATCH_SIZE: 2

CHECKPOINT_PERIOD: 20

MAX_ITER: 80

TEST:

EVAL_PERIOD: 40

VIS_PERIOD: 20

TIME_SCALE: "iter"

OUTPUT_DIR: "results://tests/basic"

VERSION: 1

[01/08 07:37:54] meddlr INFO: Running with full config:

AUG_TEST:

UNDERSAMPLE:

ACCELERATIONS: (6,)

AUG_TRAIN:

MOTION_P: 0.2

MRI_RECON:

AUG_SENSITIVITY_MAPS: True

SCHEDULER_P:

IGNORE: False

TRANSFORMS: ()

NOISE_P: 0.2

UNDERSAMPLE:

ACCELERATIONS: (6,)

CALIBRATION_SIZE: 20

CENTER_FRACTIONS: ()

MAX_ATTEMPTS: 30

NAME: PoissonDiskMaskFunc

USE_MOTION: 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: False

BATCH_BY: ()

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:

TEST: ('mridata_knee_2019_test',)

TRAIN: ('mridata_knee_2019_train',)

VAL: ('mridata_knee_2019_val',)

DESCRIPTION:

BRIEF:

ENTITY_NAME: ss_recon

EXP_NAME:

PROJECT_NAME: ss_recon

TAGS: ()

MODEL:

A2R:

META_ARCHITECTURE: GeneralizedUnrolledCNN

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: