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luna_p8.py
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luna_p8.py
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import numpy as np
import data_transforms
import data_iterators
import pathfinder
import lasagne as nn
from collections import namedtuple
from functools import partial
import lasagne.layers.dnn as dnn
import theano.tensor as T
import utils
restart_from_save = None
rng = np.random.RandomState(42)
# transformations
p_transform = {'patch_size': (64, 64, 64),
'mm_patch_size': (64, 64, 64),
'pixel_spacing': (1., 1., 1.)
}
p_transform_augment = {
'translation_range_z': [-16, 16],
'translation_range_y': [-16, 16],
'translation_range_x': [-16, 16],
'rotation_range_z': [-180, 180],
'rotation_range_y': [-180, 180],
'rotation_range_x': [-180, 180]
}
# data preparation function
def data_prep_function(data, patch_center, luna_annotations, pixel_spacing, luna_origin, p_transform,
p_transform_augment, **kwargs):
x, patch_annotation_tf, annotations_tf = data_transforms.transform_patch3d(data=data,
luna_annotations=luna_annotations,
patch_center=patch_center,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing,
luna_origin=luna_origin)
x = data_transforms.pixelnormHU(x)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
return x, y
data_prep_function_train = partial(data_prep_function, p_transform_augment=p_transform_augment, p_transform=p_transform)
data_prep_function_valid = partial(data_prep_function, p_transform_augment=None, p_transform=p_transform)
# data iterators
batch_size = 4
nbatches_chunk = 8
chunk_size = batch_size * nbatches_chunk
train_valid_ids = utils.load_pkl(pathfinder.LUNA_VALIDATION_SPLIT_PATH)
train_pids, valid_pids = train_valid_ids['train'], train_valid_ids['valid']
train_data_iterator = data_iterators.PatchPositiveLunaDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,
batch_size=chunk_size,
transform_params=p_transform,
data_prep_fun=data_prep_function_train,
rng=rng,
patient_ids=train_pids,
full_batch=True, random=True, infinite=True)
valid_data_iterator = data_iterators.ValidPatchPositiveLunaDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,
transform_params=p_transform,
data_prep_fun=data_prep_function_valid,
patient_ids=valid_pids)
nchunks_per_epoch = train_data_iterator.nsamples / chunk_size
max_nchunks = nchunks_per_epoch * 30
validate_every = int(2. * nchunks_per_epoch)
save_every = int(0.5 * nchunks_per_epoch)
learning_rate_schedule = {
0: 1e-5,
int(max_nchunks * 0.4): 5e-6,
int(max_nchunks * 0.5): 2e-6,
int(max_nchunks * 0.85): 1e-6,
int(max_nchunks * 0.95): 5e-7
}
# model
conv3d = partial(dnn.Conv3DDNNLayer,
filter_size=3,
pad='valid',
W=nn.init.Orthogonal('relu'),
b=nn.init.Constant(0.0),
nonlinearity=nn.nonlinearities.identity)
max_pool3d = partial(dnn.MaxPool3DDNNLayer,
pool_size=2)
def conv_prelu_layer(l_in, n_filters):
l = conv3d(l_in, n_filters)
l = nn.layers.ParametricRectifierLayer(l)
return l
def build_model(patch_size=None):
patch_size = p_transform['patch_size'] if patch_size is None else patch_size
l_in = nn.layers.InputLayer((None, 1,) + patch_size)
l_target = nn.layers.InputLayer((None, 1,) + patch_size)
net = {}
base_n_filters = 128
net['contr_1_1'] = conv_prelu_layer(l_in, base_n_filters)
net['contr_1_2'] = conv_prelu_layer(net['contr_1_1'], base_n_filters)
net['contr_1_3'] = conv_prelu_layer(net['contr_1_2'], base_n_filters)
net['pool1'] = max_pool3d(net['contr_1_3'])
net['encode_1'] = conv_prelu_layer(net['pool1'], base_n_filters)
net['encode_2'] = conv_prelu_layer(net['encode_1'], base_n_filters)
net['encode_3'] = conv_prelu_layer(net['encode_2'], base_n_filters)
net['encode_4'] = conv_prelu_layer(net['encode_3'], base_n_filters)
net['upscale1'] = nn.layers.Upscale3DLayer(net['encode_4'], 2)
net['concat1'] = nn.layers.ConcatLayer([net['upscale1'], net['contr_1_3']],
cropping=(None, None, "center", "center", "center"))
net['dropout_1'] = nn.layers.dropout_channels(net['concat1'], p=0.25)
net['expand_1_1'] = conv_prelu_layer(net['dropout_1'], 2 * base_n_filters)
net['expand_1_2'] = conv_prelu_layer(net['expand_1_1'], base_n_filters)
net['expand_1_3'] = conv_prelu_layer(net['expand_1_2'], base_n_filters)
net['expand_1_4'] = conv_prelu_layer(net['expand_1_3'], base_n_filters)
net['expand_1_5'] = conv_prelu_layer(net['expand_1_4'], base_n_filters)
l_out = dnn.Conv3DDNNLayer(net['expand_1_5'], num_filters=1,
filter_size=1,
nonlinearity=nn.nonlinearities.sigmoid)
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
def build_objective(model, deterministic=False, epsilon=1e-12):
network_predictions = nn.layers.get_output(model.l_out, deterministic=deterministic)[:, 0, :, :, :]
target_values = nn.layers.get_output(model.l_target)[:, 0, :, :, :]
network_predictions, target_values = nn.layers.merge.autocrop([network_predictions, target_values],
[None, 'center', 'center', 'center'])
y_true_f = target_values
y_pred_f = network_predictions
intersection = T.sum(y_true_f * y_pred_f, axis=(1, 2, 3))
dice_batch = (2. * intersection + epsilon) / (
T.sum(y_true_f, axis=(1, 2, 3)) + T.sum(y_pred_f, axis=(1, 2, 3)) + epsilon)
return -1. * T.sum(dice_batch)
def build_updates(train_loss, model, learning_rate):
updates = nn.updates.adam(train_loss, nn.layers.get_all_params(model.l_out), learning_rate)
return updates