#same as r_elias_4 but with mse
import numpy as np
import data_transforms
import data_iterators
import pathfinder
import lasagne as nn
from collections import OrderedDict, namedtuple
from functools import partial
import lasagne.layers.dnn as dnn
import lasagne
import theano.tensor as T
import utils
restart_from_save = False
rng = np.random.RandomState(33)
# transformations
p_transform = {'patch_size': (48, 48, 48),
'mm_patch_size': (48, 48, 48),
'pixel_spacing': (1., 1., 1.)
}
p_transform_augment = {
'translation_range_z': [-3, 3],
'translation_range_y': [-3, 3],
'translation_range_x': [-3, 3],
'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.hu2normHU(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)
alternative_view = False
# data iterators
batch_size = 16
nbatches_chunk = 1
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']
order_objectives = ['nodule',
'size',
'spiculation',
'sphericity',
'calcification',
'subtlety',
'malignancy',
'lobulation',
'texture',
'margin']
property_type = {'nodule': 'classification',
'size': 'continuous',
'spiculation': 'continuous',
'sphericity': 'continuous', # from linear to round
'calcification': 'classification', # 0: nothing, 1: popcorn, 2:laminated, 3:solid, 4:non-central, 5:central,6:absent
'subtlety': 'continuous',
'malignancy': 'continuous',
'lobulation': 'classification',
'texture': 'classification', # 0:nothing, 1: non-solid/ground glass, 2: mixed, 3: solid
'margin': 'continuous'} # from poorly defined to sharp
property_bin_borders = {'nodule': [0.5,1.1], #little hack for having two classes
'calcification': [1.5,2.5,3.5,4.5,5.5,100],
'lobulation': [1.5,2.5,3.5,4.5,100],
'texture': [1.5,2.5,3.5,4.5,100]}
norm_weights_loss = {'nodule': 1., #class
'size': 5.,
'spiculation': 1.,
'sphericity': 2.,
'calcification': 1., #class
'subtlety': 2.,
'malignancy': 1., #because it is important
'lobulation': 1., #class
'texture': 1., #class
'margin': 2.}
init_values_final_units = {
'nodule': .1, #class
'size': .1,
'spiculation': .1,
'sphericity': .1,
'calcification': .1, #class
'subtlety': .1,
'malignancy': .1,
'lobulation': .1, #class
'texture': .1, #class
'margin': .1}
train_data_iterator = data_iterators.CandidatesLunaPropsDataGenerator(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_valid_ids['train'],
full_batch=True, random=True, infinite=True,
positive_proportion=0.8,
order_objectives = order_objectives,
property_bin_borders = property_bin_borders,
return_enable_target_vector = True)
valid_data_iterator = data_iterators.CandidatesLunaPropsValidDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,
transform_params=p_transform,
data_prep_fun=data_prep_function_valid,
patient_ids=train_valid_ids['valid'],
order_objectives = order_objectives,
property_bin_borders = property_bin_borders,
return_enable_target_vector = True)
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(5. * nchunks_per_epoch)
learning_rate_schedule = {
0: 4e-4,
int(max_nchunks * 0.5): 1e-4,
int(max_nchunks * 0.6): 5e-5,
int(max_nchunks * 0.7): 2.5e-5,
int(max_nchunks * 0.8): 1.25e-5,
int(max_nchunks * 0.9): 0.625e-5
}
# model
conv3d = partial(dnn.Conv3DDNNLayer,
filter_size=3,
pad='same',
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.01),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
max_pool3d = partial(dnn.MaxPool3DDNNLayer,
pool_size=2)
drop = lasagne.layers.DropoutLayer
bn = lasagne.layers.batch_norm
dense = partial(lasagne.layers.DenseLayer,
W=lasagne.init.Orthogonal('relu'),
b=lasagne.init.Constant(0.0),
nonlinearity=lasagne.nonlinearities.rectify)
def inrn_v2(lin):
n_base_filter = 32
l1 = conv3d(lin, n_base_filter, filter_size=1)
l2 = conv3d(lin, n_base_filter, filter_size=1)
l2 = conv3d(l2, n_base_filter, filter_size=3)
l3 = conv3d(lin, n_base_filter, filter_size=1)
l3 = conv3d(l3, n_base_filter, filter_size=3)
l3 = conv3d(l3, n_base_filter, filter_size=3)
l = lasagne.layers.ConcatLayer([l1, l2, l3])
l = conv3d(l, lin.output_shape[1], filter_size=1)
l = lasagne.layers.ElemwiseSumLayer([l, lin])
l = lasagne.layers.NonlinearityLayer(l, nonlinearity=lasagne.nonlinearities.rectify)
return l
def inrn_v2_red(lin):
# We want to reduce our total volume /4
den = 16
nom2 = 4
nom3 = 5
nom4 = 7
ins = lin.output_shape[1]
l1 = max_pool3d(lin)
l2 = conv3d(lin, ins // den * nom2, filter_size=3, stride=2)
l3 = conv3d(lin, ins // den * nom2, filter_size=1)
l3 = conv3d(l3, ins // den * nom3, filter_size=3, stride=2)
l4 = conv3d(lin, ins // den * nom2, filter_size=1)
l4 = conv3d(l4, ins // den * nom3, filter_size=3)
l4 = conv3d(l4, ins // den * nom4, filter_size=3, stride=2)
l = lasagne.layers.ConcatLayer([l1, l2, l3, l4])
return l
def feat_red(lin):
# We want to reduce the feature maps by a factor of 2
ins = lin.output_shape[1]
l = conv3d(lin, ins // 2, filter_size=1)
return l
no_properties = len(order_objectives)
def build_model():
l_in = nn.layers.InputLayer((None, ) + p_transform['patch_size'])
l_ds = nn.layers.DimshuffleLayer(l_in, pattern=[0,'x',1,2,3])
l_target = nn.layers.InputLayer((None, no_properties))
l_enable_target = nn.layers.InputLayer((None, no_properties))
l = conv3d(l_ds, 64)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = nn.layers.GlobalPoolLayer(l)
final_layers = []
unit_ptr = 0
for obj_idx, obj_name in enumerate(order_objectives):
ptype = property_type[obj_name]
if ptype == 'classification':
num_units = len(property_bin_borders[obj_name])
l_fin = nn.layers.DenseLayer(l, num_units=num_units,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(init_values_final_units[obj_name]),
nonlinearity=nn.nonlinearities.softmax, name='dense_'+ptype+'_'+obj_name)
elif ptype == 'continuous':
l_fin = nn.layers.DenseLayer(l, num_units=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(init_values_final_units[obj_name]),
nonlinearity=nn.nonlinearities.softplus, name='dense_'+ptype+'_'+obj_name)
else:
raise
final_layers.append(l_fin)
l_out = nn.layers.ConcatLayer(final_layers, name = 'final_concat_layer')
return namedtuple('Model', ['l_in', 'l_out', 'l_target', 'l_enable_target'])(l_in, l_out, l_target, l_enable_target)
d_objectives_deterministic = {}
d_objectives = {}
def sqe(target_idx, prediction_idx, predictions, targets):
predictions = predictions[:,prediction_idx]
targets = targets[:,target_idx]
out = nn.objectives.squared_error(predictions,targets)
return out
def cce(target_idx, prediction_idcs, predictions, targets, epsilon):
predictions = predictions[:,prediction_idcs[0]:prediction_idcs[1]]
predictions = T.cast(T.clip(predictions, epsilon, 1.-epsilon), 'float32')
targets = T.cast(targets[:,target_idx], 'int32')
cc = nn.objectives.categorical_crossentropy(predictions,targets)
return cc
def build_objective(model, deterministic=False, epsilon=1e-12):
predictions = nn.layers.get_output(model.l_out, deterministic=deterministic)
targets = nn.layers.get_output(model.l_target)
enable_targets = nn.layers.get_output(model.l_enable_target)
sum_of_objectives = 0
unit_ptr = 0
for obj_idx, obj_name in enumerate(order_objectives):
ptype = property_type[obj_name]
if ptype == 'classification':
num_units = len(property_bin_borders[obj_name])
v_obj = cce(obj_idx, (unit_ptr, unit_ptr+num_units), predictions, targets, epsilon)
# take the mean of the objectives where it matters (enabled targets)
obj_scalar = T.sum(enable_targets[:,obj_idx] * v_obj) / (0.00001 + T.sum(enable_targets[:,obj_idx]))
unit_ptr = unit_ptr + num_units
elif ptype == 'continuous':
v_obj = sqe(obj_idx, unit_ptr, predictions, targets)
obj_scalar = T.mean(v_obj)
unit_ptr += 1
else:
raise
if deterministic:
d_objectives_deterministic[obj_name] = obj_scalar
else:
d_objectives[obj_name] = obj_scalar
sum_of_objectives += norm_weights_loss[obj_name] * obj_scalar
return sum_of_objectives
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
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