'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']
)
# model
model = nn.Sequential(
'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),
apply_at(1, np.float32),
batch_size=CONFIG['batch_size'], batches_per_epoch=CONFIG['batches_per_epoch'], combiner=combine_pad
)