def getBatches(*args):
q, p_number, config = args
import random
random.seed(p_number)
atlas, itk_atlas = loadAtlas(config)
data = loadOASISData()
train, test = data[:int(len(data) * config['split']
)], data[int(len(data) * config['split']):]
volume_shape = config['resolution']
data_train = train[int(len(train) * config['validation']):]
while True:
minibatch = np.empty(shape=(config['batchsize'], *volume_shape, 2))
for i in range(config['batchsize']):
idx_volume = random.choice(list(range(len(data_train))))
vol = readNormalizedVolumeByPath(data_train[idx_volume]['img'],
itk_atlas)
minibatch[i, :, :, :,
0] = atlas.reshape(volume_shape).astype("float32")
minibatch[i, :, :, :,
1] = vol.reshape(volume_shape).astype("float32")
q.put(minibatch)
def getTestData(config):
atlas, itk_atlas = loadAtlas(config)
data = loadOASISData()
data_test = data[int(len(data) * config['split']):]
volume_shape = config['resolution']
l = len(data_test)
test = np.empty(shape=(l, *volume_shape, 2))
for i in range(l):
vol = readNormalizedVolumeByPath(data_test[i]['img'], itk_atlas)
test[i, :, :, :, 0] = atlas.reshape(volume_shape).astype("float32")
test[i, :, :, :, 1] = vol.reshape(volume_shape).astype("float32")
return test
def getValidationData(config):
atlas, itk_atlas = loadAtlas(config)
data = loadOASISData()
train, test = data[:int(len(data) * config['split']
)], data[int(len(data) * config['split']):]
volume_shape = config['resolution']
data_val = train[:int(len(train) * config['validation'])]
l = len(data_val)
val = np.empty(shape=(l, *volume_shape, 2))
for i in range(l):
vol = readNormalizedVolumeByPath(data_val[i]['img'], itk_atlas)
val[i, :, :, :, 0] = atlas.reshape(volume_shape).astype("float32")
val[i, :, :, :, 1] = vol.reshape(volume_shape).astype("float32")
return val
import multiprocessing as mp
#mp.set_start_method("spawn")
train_config = {
'batchsize': 1,
'split': 0.9,
'validation': 0.1,
'half_res': True,
'epochs': 200,
'atlas': 'atlas.nii.gz',
'model_output': 'model.pkl',
'exponentialSteps': 7,
}
training_elements = int(
len(loadOASISData()) * train_config['split'] *
(1 - train_config['validation']))
data_queue, processes = DataGenerator.stream(2, 1, train_config)
validation_data = DataGenerator.getValidationData(train_config)
validation_data_y = DataGenerator.inferYFromBatch(validation_data,
train_config)
def train_generator():
while True:
minibatch = data_queue.get()
yield minibatch, DataGenerator.inferYFromBatch(minibatch, train_config)
