Python CropToBrain Beispiele

Beispiel #1

    '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(
        .5,
        unpack_args(lambda image, gt: (np.flip(image, 1), np.flip(gt, 0)))),
    apply_at(1, prepend_dims),

Beispiel #2

Datei: unet_3d.py Projekt: migonch/two_and_half_d

    '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']
)

# model

Beispiel #3

Datei: unet_2_5d.py Projekt: migonch/two_and_half_d

    'n_slices': 15,
    'batch_size': 3,
    'batches_per_epoch': 100,
    'n_epochs': 200,
    'lr': 3e-4,
    'device': 'cuda',
}

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

# 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_spacing,
                      train_dataset.load_gt,
                      ids=train_ids),