Python cache_methods Examples

Example #1

File: unet_3d.py Project: migonch/two_and_half_d

    '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(

Example #2

    '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
)