Exemplos de BraTS2013 em Python

Exemplo n.º 1

Arquivo: unet_3d.py Projeto: migonch/two_and_half_d

    'batch_size': 5,
    'batches_per_epoch': 100,
    'n_epochs': 300,
    'lr': 1e-4,
    'device': 'cuda',
}

try:
    CONFIG.update(load_json(sys.argv[5]))
except (IndexError, FileNotFoundError):
    pass

PATCH_SIZE = np.array([64, 64, 32])

# dataset
raw_dataset = BraTS2013(BRATS_PATH)
dataset = apply(CropToBrain(raw_dataset), load_image=partial(min_max_scale, axes=0))
train_dataset = cache_methods(ChangeSliceSpacing(dataset, new_slice_spacing=CONFIG['source_slice_spacing']))

# cross validation
split = load_json(SPLIT_PATH)
train_ids, val_ids, test_ids = split[int(FOLD)]

# batch iterator
batch_iter = Infinite(
    load_by_random_id(train_dataset.load_image, train_dataset.load_gt, ids=train_ids),
    unpack_args(tumor_sampling, patch_size=PATCH_SIZE, tumor_p=.5),
    random_apply(.5, unpack_args(lambda image, gt: (np.flip(image, 1), np.flip(gt, 0)))),
    batch_size=CONFIG['batch_size'], batches_per_epoch=CONFIG['batches_per_epoch']
)

Exemplo n.º 2

    'batch_size': 5,
    'batches_per_epoch': 100,
    'n_epochs': 300,
    'lr': 1e-4,
    'device': 'cuda',
}

try:
    CONFIG.update(load_json(sys.argv[5]))
except (IndexError, FileNotFoundError):
    pass

PATCH_SIZE = np.array([64, 64, 32])

# dataset
raw_dataset = BinaryGT(BraTS2013(BRATS_PATH),
                       positive_classes=CONFIG['positive_classes'])
dataset = cache_methods(
    ZooOfSpacings(apply(CropToBrain(raw_dataset),
                        load_image=partial(min_max_scale, axes=0)),
                  slice_spacings=CONFIG['slice_spacings']))

# cross validation
split = load_json(SPLIT_PATH)
train_ids, val_ids, test_ids = split[int(FOLD)]

# batch iterator
batch_iter = Infinite(
    load_by_random_id(dataset.load_image, dataset.load_gt, ids=train_ids),
    unpack_args(tumor_sampling, patch_size=PATCH_SIZE, tumor_p=.5),
    random_apply(

Exemplo n.º 3

    'positive_classes': [1, 2, 3, 4],
    'slice_spacings': np.linspace(1, 3, 5),
    'batch_size': 30,
    'batches_per_epoch': 100,
    'n_epochs': 100,
    'lr': 1e-3,
    'device': 'cuda',
}

try:
    CONFIG.update(load_json(sys.argv[5]))
except (IndexError, FileNotFoundError):
    pass

# dataset
raw_dataset = BinaryGT(BraTS2013(BRATS_PATH), positive_classes=CONFIG['positive_classes'])
dataset = cache_methods(ZooOfSpacings(
    apply(CropToBrain(raw_dataset), load_image=partial(min_max_scale, axes=0)),
    slice_spacings=CONFIG['slice_spacings']
))

# cross validation
split = load_json(SPLIT_PATH)
train_ids, val_ids, test_ids = split[int(FOLD)]

# batch iterator
batch_iter = Infinite(
    load_by_random_id(dataset.load_image, dataset.load_gt, ids=train_ids),
    unpack_args(get_random_slice),
    random_apply(.5, unpack_args(lambda image, gt: (np.flip(image, 1), np.flip(gt, 0)))),
    apply_at(1, prepend_dims),

Exemplo n.º 4

Arquivo: save_split.py Projeto: migonch/two_and_half_d

import sys
from pathlib import Path

from dpipe.split import train_val_test_split
from dpipe.io import save_json

from two_and_half_d.dataset import BraTS2013

BRATS_PATH = Path(sys.argv[1])
SPLIT_PATH = Path(sys.argv[2])
VAL_SIZE = 1
N_FOLDS = 5
RANDOM_STATE = 42

save_json(
    train_val_test_split(BraTS2013(BRATS_PATH).ids,
                         val_size=VAL_SIZE,
                         n_splits=N_FOLDS,
                         random_state=RANDOM_STATE), SPLIT_PATH)