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luna_p7.py
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luna_p7.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]
}
zmuv_mean, zmuv_std = 0.36, 0.31
# 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.hu2normHU(x)
x = data_transforms.zmuv(x, zmuv_mean, zmuv_std)
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)
if zmuv_mean is None or zmuv_std is None:
print 'estimating ZMUV parameters'
x_big = None
for i, (x, _, _) in zip(xrange(4), train_data_iterator.generate()):
print i
x_big = x if x_big is None else np.concatenate((x_big, x), axis=0)
zmuv_mean = x_big.mean()
zmuv_std = x_big.std()
print 'mean:', zmuv_mean
print 'std:', zmuv_std
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 * 0.5): 5e-6,
int(max_nchunks * 0.8): 2e-6,
int(max_nchunks * 0.9): 1e-6
}
# model
conv3d = partial(dnn.Conv3DDNNLayer,
W=nn.init.Orthogonal('relu'),
b=nn.init.Constant(0.0),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
max_pool3d = partial(dnn.MaxPool3DDNNLayer,
pool_size=2)
def conv_prelu_layer(l_in, n_filters, filter_size=3, pad='valid'):
l = conv3d(l_in, n_filters, filter_size=filter_size, pad=pad)
l = nn.layers.ParametricRectifierLayer(l)
return l
def build_model():
l_in = nn.layers.InputLayer((None, 1,) + p_transform['patch_size'])
l_target = nn.layers.InputLayer((None, 1,) + p_transform['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['contr_1_4'] = conv_prelu_layer(net['contr_1_3'], base_n_filters)
net['pool1'] = max_pool3d(net['contr_1_4'])
net['encode_0_1'] = conv_prelu_layer(net['pool1'], base_n_filters)
net['encode_0_2'] = conv_prelu_layer(net['encode_0_1'], base_n_filters)
net['encode_0_3'] = conv_prelu_layer(net['encode_0_2'], base_n_filters)
net['encode_0_4'] = conv_prelu_layer(net['encode_0_3'], base_n_filters)
net['encode_1_1'] = conv_prelu_layer(net['pool1'], base_n_filters, filter_size=1)
net['encode_1_2'] = conv_prelu_layer(net['encode_1_1'], base_n_filters, filter_size=3)
net['encode_2_1'] = conv_prelu_layer(net['pool1'], base_n_filters, filter_size=1)
net['encode'] = nn.layers.ConcatLayer([net['encode_0_4'], net['encode_1_2'], net['encode_2_1']],
cropping=(None, None, "center", "center", "center"))
net['upscale1'] = nn.layers.Upscale3DLayer(net['encode'], 2)
net['concat1'] = nn.layers.ConcatLayer([net['upscale1'], net['contr_1_4']],
cropping=(None, None, "center", "center", "center"))
net['dropout_1'] = nn.layers.dropout_channels(net['concat1'], p=0.3)
net['expand_1_1'] = conv_prelu_layer(net['dropout_1'], 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)
l_out = dnn.Conv3DDNNLayer(net['expand_1_4'], 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)
target_values = nn.layers.get_output(model.l_target)
network_predictions, target_values = nn.layers.merge.autocrop([network_predictions, target_values],
[None, None, 'center', 'center', 'center'])
y_true_f = target_values
y_pred_f = network_predictions
intersection = T.sum(y_true_f * y_pred_f)
dice = (2 * intersection + epsilon) / (T.sum(y_true_f) + T.sum(y_pred_f) + epsilon)
return -1. * dice
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