flip_vertical_p=0.5,
affine_p=1.,
affine_scale_choices=np.linspace(0.5, 1.5, 11),
# affine_shear_choices=np.linspace(-0.5, 0.5, 11),
affine_translation_choices=np.arange(-64, 64, 1),
# affine_rotation_choices=np.arange(0, 360, 1),
adjust_gamma_p=0.5,
adjust_gamma_chocies=np.linspace(0.5, 1.5, 11))
train_iterator = TrainIterator(**train_iterator_kwargs)
test_iterator_kwargs = dict(batch_size=batch_size, )
test_iterator = TestIterator(**test_iterator_kwargs)
save_weights = SaveWeights(model_fname, only_best=True, pickle=False)
save_training_history = SaveTrainingHistory(model_history_fname)
plot_training_history = PlotTrainingHistory(model_graph_fname)
early_stopping = EarlyStopping(patience=100)
conv_kwargs = dict(pad='same',
nonlinearity=nn.nonlinearities.very_leaky_rectify)
pool_kwargs = dict(pool_size=2, )
l = nn.layers.InputLayer(name='in', shape=(None, 3, image_size, image_size))
# 256
l = conv2dbn(l,
name='l1c1',
num_filters=32,
filter_size=(7, 7),
stride=2,
'affine_rotation_choices': np.arange(-45, 50, 2.5),
}
train_iterator = TrainIterator(**train_iterator_kwargs)
test_iterator_kwargs = {
'read_image_size': (image_size, image_size),
'read_image_as_gray': False,
'read_image_prefix_path': '',
'batch_size': batch_size,
'buffer_size': 2,
}
test_iterator = TestIterator(**test_iterator_kwargs)
save_weights = SaveWeights('model_weights.pkl', only_best=True, pickle=False)
save_training_history = SaveTrainingHistory('model_history.pkl')
plot_training_history = PlotTrainingHistory('training_history.png')
#early_stopping = EarlyStopping(metrics='valid_accuracy', patience=100, verbose=True, higher_is_better=True) BACKLOG:0 Implement early stopping
def save_to_json(nn, training_history):
train_hist = []
for row in training_history:
new_row = {}
new_row["epoch"] = row["epoch"]
new_row["train_loss"] = row["train_loss"]
new_row["valid_loss"] = row["valid_loss"]
new_row["valid_accuracy"] = row["valid_accuracy"]
new_row["max_epochs"] = nn.max_epochs
new_row["estimate"] = (nn.max_epochs - row["epoch"]) * row["dur"]
new_row["dur"] = row["dur"]
new_row["t_next"] = int(row["dur"]) + time.time()
'affine_rotation_choices': np.arange(-45, 50, 5),
}
train_iterator = TrainIterator(**train_iterator_kwargs)
test_iterator_kwargs = {
'batch_size': batch_size,
'buffer_size': 5,
}
test_iterator = TestIterator(**test_iterator_kwargs)
save_weights = SaveWeights('./examples/mnist/model_weights.pkl',
only_best=True,
pickle=False)
save_training_history = SaveTrainingHistory(
'./examples/mnist/model_history.pkl')
plot_training_history = PlotTrainingHistory(
'./examples/mnist/training_history.png')
early_stopping = EarlyStopping(metrics='valid_accuracy',
patience=100,
verbose=True,
higher_is_better=True)
net = NeuralNet(layers=[
(InputLayer, dict(name='in', shape=(None, 1, image_size, image_size))),
(Conv2DDNNLayer,
dict(name='l1c1', num_filters=32, filter_size=(3, 3), pad='same')),
(Conv2DDNNLayer,
dict(name='l1c2', num_filters=32, filter_size=(3, 3), pad='same')),
(MaxPool2DDNNLayer, dict(name='l1p', pool_size=3, stride=2)),
(Conv2DDNNLayer,
dict(name='l2c1', num_filters=32, filter_size=(3, 3), pad='same')),
(Conv2DDNNLayer,
test_iterator_kwargs = {
'buffer_size': 5,
'batch_size': batch_size,
'read_image_size': (image_size, image_size),
'read_image_as_gray': False,
'read_image_prefix_path': './examples/cifar10/data/train/',
}
test_iterator = TestIterator(**test_iterator_kwargs)
save_weights = SaveWeights('./examples/cifar10/model_weights.pkl',
only_best=True,
pickle=False)
save_training_history = SaveTrainingHistory(
'./examples/cifar10/model_history.pkl')
plot_training_history = PlotTrainingHistory(
'./examples/cifar10/training_history.png')
net = NeuralNet(
layers=[
(InputLayer, dict(name='in', shape=(None, 3, image_size, image_size))),
(Conv2DDNNLayer,
dict(name='l1c1', num_filters=16, filter_size=(3, 3), pad='same')),
(Conv2DDNNLayer,
dict(name='l1c2', num_filters=16, filter_size=(3, 3), pad='same')),
(Conv2DDNNLayer,
dict(name='l1c3', num_filters=32, filter_size=(3, 3), pad='same')),
(Conv2DDNNLayer,
dict(name='l1c4', num_filters=32, filter_size=(3, 3), pad='same')),
(MaxPool2DDNNLayer, dict(name='l1p', pool_size=3, stride=2)),
(Conv2DDNNLayer,
dict(name='l2c1', num_filters=32, filter_size=(3, 3), pad='same')),
def main():
c = color_codes()
patch_size = (15, 15, 15)
dir_name = '/home/sergivalverde/w/CNN/images/CH16'
patients = [
f for f in sorted(os.listdir(dir_name))
if os.path.isdir(os.path.join(dir_name, f))
]
names = np.stack([
name for name in
[[
os.path.join(dir_name, patient, 'FLAIR_preprocessed.nii.gz')
for patient in patients
],
[
os.path.join(dir_name, patient, 'DP_preprocessed.nii.gz')
for patient in patients
],
[
os.path.join(dir_name, patient, 'T2_preprocessed.nii.gz')
for patient in patients
],
[
os.path.join(dir_name, patient, 'T1_preprocessed.nii.gz')
for patient in patients
]] if name is not None
],
axis=1)
seed = np.random.randint(np.iinfo(np.int32).max)
''' Here we create an initial net to find conflictive voxels '''
print(c['c'] + '[' + strftime("%H:%M:%S") + '] ' + c['g'] +
'<Running iteration ' + c['b'] + '1>' + c['nc'])
net_name = '/home/sergivalverde/w/CNN/code/CNN1/miccai_challenge2016/deep-challenge2016.init.'
net = NeuralNet(
layers=[
(InputLayer, dict(name='in', shape=(None, 4, 15, 15, 15))),
(Conv3DDNNLayer,
dict(name='conv1_1',
num_filters=32,
filter_size=(5, 5, 5),
pad='same')),
(Pool3DDNNLayer,
dict(name='avgpool_1',
pool_size=2,
stride=2,
mode='average_inc_pad')),
(Conv3DDNNLayer,
dict(name='conv2_1',
num_filters=64,
filter_size=(5, 5, 5),
pad='same')),
(Pool3DDNNLayer,
dict(name='avgpool_2',
pool_size=2,
stride=2,
mode='average_inc_pad')),
(DropoutLayer, dict(name='l2drop', p=0.5)),
(DenseLayer, dict(name='l1', num_units=256)),
(DenseLayer,
dict(name='out', num_units=2,
nonlinearity=nonlinearities.softmax)),
],
objective_loss_function=objectives.categorical_crossentropy,
update=updates.adam,
update_learning_rate=0.0001,
on_epoch_finished=[
SaveWeights(net_name + 'model_weights.pkl',
only_best=True,
pickle=False),
SaveTrainingHistory(net_name + 'model_history.pkl'),
PlotTrainingHistory(net_name + 'training_history.png'),
EarlyStopping(patience=10)
],
verbose=10,
max_epochs=50,
train_split=TrainSplit(eval_size=0.25),
custom_scores=[('dsc', lambda p, t: 2 * np.sum(p * t[:, 1]) / np.sum(
(p + t[:, 1])))],
)
try:
net.load_params_from(net_name + 'model_weights.pkl')
except IOError:
print(c['c'] + '[' + strftime("%H:%M:%S") + '] ' + c['g'] +
'Loading the data for ' + c['b'] + 'iteration 1' + c['nc'])
# Create the data
(x, y, _) = load_patches(dir_name=dir_name,
use_flair=True,
use_pd=True,
use_t2=True,
use_t1=True,
use_gado=False,
flair_name='FLAIR_preprocessed.nii.gz',
pd_name='DP_preprocessed.nii.gz',
t2_name='T2_preprocessed.nii.gz',
t1_name='T1_preprocessed.nii.gz',
gado_name=None,
mask_name='Consensus.nii.gz',
size=patch_size)
print('-- Permuting the data')
np.random.seed(seed)
x_train = np.random.permutation(
np.concatenate(x).astype(dtype=np.float32))
print('-- Permuting the labels')
np.random.seed(seed)
y_train = np.random.permutation(
np.concatenate(y).astype(dtype=np.int32))
y_train = y_train[:, y_train.shape[1] / 2 + 1,
y_train.shape[2] / 2 + 1, y_train.shape[3] / 2 + 1]
print('-- Training vector shape = (' +
','.join([str(length) for length in x_train.shape]) + ')')
print('-- Training labels shape = (' +
','.join([str(length) for length in y_train.shape]) + ')')
print c['c'] + '[' + strftime("%H:%M:%S") + '] ' + c['g'] +\
'Training (' + c['b'] + 'initial' + c['nc'] + c['g'] + ')' + c['nc']
# We try to get the last weights to keep improving the net over and over
net.fit(x_train, y_train)
''' Here we get the seeds '''
print c['c'] + '[' + strftime(
"%H:%M:%S") + '] ' + c['g'] + '<Looking for seeds>' + c['nc']
for patient in names:
output_name = os.path.join('/'.join(patient[0].rsplit('/')[:-1]),
'test.iter1.nii.gz')
try:
load_nii(output_name)
print c['c'] + '[' + strftime("%H:%M:%S") + '] ' \
+ c['g'] + '-- Patient ' + patient[0].rsplit('/')[-2] + ' already done' + c['nc']
except IOError:
print c['c'] + '[' + strftime("%H:%M:%S") + '] '\
+ c['g'] + '-- Testing with patient ' + c['b'] + patient[0].rsplit('/')[-2] + c['nc']
image_nii = load_nii(patient[0])
image = np.zeros_like(image_nii.get_data())
for batch, centers in load_patch_batch(patient, 100000,
patch_size):
y_pred = net.predict_proba(batch)
[x, y, z] = np.stack(centers, axis=1)
image[x, y, z] = y_pred[:, 1]
print c['g'] + '-- Saving image ' + c['b'] + output_name + c['nc']
image_nii.get_data()[:] = image
image_nii.to_filename(output_name)
''' Here we perform the last iteration '''
print c['c'] + '[' + strftime("%H:%M:%S") + '] ' + c[
'g'] + '<Running iteration ' + c['b'] + '2>' + c['nc']
net_name = '/home/sergivalverde/w/CNN/code/CNN1/miccai_challenge2016/deep-challenge2016.final.'
net = NeuralNet(
layers=[
(InputLayer, dict(name='in', shape=(None, 4, 15, 15, 15))),
(Conv3DDNNLayer,
dict(name='conv1_1',
num_filters=32,
filter_size=(5, 5, 5),
pad='same')),
(Pool3DDNNLayer,
dict(name='avgpool_1',
pool_size=2,
stride=2,
mode='average_inc_pad')),
(Conv3DDNNLayer,
dict(name='conv2_1',
num_filters=64,
filter_size=(5, 5, 5),
pad='same')),
(Pool3DDNNLayer,
dict(name='avgpool_2',
pool_size=2,
stride=2,
mode='average_inc_pad')),
(DropoutLayer, dict(name='l2drop', p=0.5)),
(DenseLayer, dict(name='l1', num_units=256)),
(DenseLayer,
dict(name='out', num_units=2,
nonlinearity=nonlinearities.softmax)),
],
objective_loss_function=objectives.categorical_crossentropy,
update=updates.adam,
update_learning_rate=0.0001,
on_epoch_finished=[
SaveWeights(net_name + 'model_weights.pkl',
only_best=True,
pickle=False),
SaveTrainingHistory(net_name + 'model_history.pkl'),
PlotTrainingHistory(net_name + 'training_history.png'),
],
batch_iterator_train=BatchIterator(batch_size=4096),
verbose=10,
max_epochs=2000,
train_split=TrainSplit(eval_size=0.25),
custom_scores=[('dsc', lambda p, t: 2 * np.sum(p * t[:, 1]) / np.sum(
(p + t[:, 1])))],
)
try:
net.load_params_from(net_name + 'model_weights.pkl')
except IOError:
pass
print c['c'] + '[' + strftime("%H:%M:%S") + '] '\
+ c['g'] + 'Loading the data for ' + c['b'] + 'iteration 2' + c['nc']
(x, y,
names) = load_patches(dir_name='/home/sergivalverde/w/CNN/images/CH16',
use_flair=True,
use_pd=True,
use_t2=True,
use_t1=True,
use_gado=False,
flair_name='FLAIR_preprocessed.nii.gz',
pd_name='DP_preprocessed.nii.gz',
t2_name='T2_preprocessed.nii.gz',
gado_name=None,
t1_name='T1_preprocessed.nii.gz',
mask_name='Consensus.nii.gz',
size=patch_size,
roi_name='test.iter1.nii.gz')
print '-- Permuting the data'
np.random.seed(seed)
x_train = np.random.permutation(np.concatenate(x).astype(dtype=np.float32))
print '-- Permuting the labels'
np.random.seed(seed)
y_train = np.random.permutation(np.concatenate(y).astype(dtype=np.int32))
y_train = y_train[:, y_train.shape[1] / 2 + 1, y_train.shape[2] / 2 + 1,
y_train.shape[3] / 2 + 1]
print '-- Training vector shape = (' + ','.join(
[str(length) for length in x_train.shape]) + ')'
print '-- Training labels shape = (' + ','.join(
[str(length) for length in y_train.shape]) + ')'
print c['c'] + '[' + strftime("%H:%M:%S") + '] '\
+ c['g'] + 'Training (' + c['b'] + 'final' + c['nc'] + c['g'] + ')' + c['nc']
net.fit(x_train, y_train)