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 lasagne
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': [-4, 4],
'translation_range_y': [-4, 4],
'translation_range_x': [-4, 4],
'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, pixel_spacing, luna_origin, p_transform,
p_transform_augment, world_coord_system, **kwargs):
x, patch_annotation_tf = data_transforms.transform_patch3d(data=data,
luna_annotations=None,
patch_center=patch_center,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing,
luna_origin=luna_origin,
world_coord_system=world_coord_system)
x = data_transforms.pixelnormHU(x)
return x
data_prep_function_train = partial(data_prep_function, p_transform_augment=p_transform_augment,
p_transform=p_transform, world_coord_system=True)
data_prep_function_valid = partial(data_prep_function, p_transform_augment=None,
p_transform=p_transform, world_coord_system=True)
# 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.CandidatesLunaDataGenerator(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,
positive_proportion=0.5)
valid_data_iterator = data_iterators.CandidatesLunaValidDataGenerator(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 * 100
validate_every = int(5. * nchunks_per_epoch)
save_every = int(1. * nchunks_per_epoch)
learning_rate_schedule = {
0: 1e-5,
int(max_nchunks * 0.5): 5e-6,
int(max_nchunks * 0.6): 2e-6,
int(max_nchunks * 0.8): 1e-6,
int(max_nchunks * 0.9): 5e-7
}
# model
conv3 = partial(dnn.Conv3DDNNLayer,
filter_size=3,
pad='valid',
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.01),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
max_pool = partial(dnn.MaxPool3DDNNLayer,
pool_size=2)
drop = lasagne.layers.DropoutLayer
dense = partial(lasagne.layers.DenseLayer,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.01),
nonlinearity=lasagne.nonlinearities.very_leaky_rectify)
def build_model():
l_in = nn.layers.InputLayer((None, 1,) + p_transform['patch_size'])
l_target = nn.layers.InputLayer((None, 1))
l = conv3(l_in, num_filters=128)
l = conv3(l, num_filters=128)
l = max_pool(l)
l = conv3(l, num_filters=128)
l = conv3(l, num_filters=128)
l = max_pool(l)
l = conv3(l, num_filters=256)
l = conv3(l, num_filters=256)
l = conv3(l, num_filters=256)
l = max_pool(l)
l_d01 = nn.layers.DenseLayer(l, num_units=1024, W=nn.init.Orthogonal(),
b=nn.init.Constant(0.01), nonlinearity=nn.nonlinearities.very_leaky_rectify)
l_d02 = nn.layers.DenseLayer(nn.layers.dropout(l_d01), num_units=1024, W=nn.init.Orthogonal(),
b=nn.init.Constant(0.01), nonlinearity=nn.nonlinearities.very_leaky_rectify)
l_out = nn.layers.DenseLayer(l_d02, num_units=2,
W=nn.init.Constant(0.),
nonlinearity=nn.nonlinearities.softmax)
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
def build_objective(model, deterministic=False, epsilon=1e-12):
predictions = nn.layers.get_output(model.l_out, deterministic=deterministic)
targets = T.cast(T.flatten(nn.layers.get_output(model.l_target)), 'int32')
p = predictions[T.arange(predictions.shape[0]), targets]
p = T.clip(p, epsilon, 1.)
loss = T.mean(T.log(p))
return -loss
def build_updates(train_loss, model, learning_rate):
updates = nn.updates.adam(train_loss, nn.layers.get_all_params(model.l_out, trainable=True), learning_rate)
return updates
Datasets
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