__author__ = 'adeb'
from shutil import copy2
import inspect
import PIL
import pickle
from spynet.utils.utilities import analyse_classes
from data_brain_parcellation import DatasetBrainParcellation, DataGeneratorBrain
from network_brain_parcellation import *
from spynet.models.network import *
from spynet.models.neuron_type import *
from spynet.data.dataset import *
from spynet.training.trainer import *
from spynet.training.monitor import *
from spynet.training.parameters_selector import *
from spynet.training.stopping_criterion import *
from spynet.training.cost_function import *
from spynet.training.learning_update import *
from spynet.experiment import Experiment
from spynet.utils.utilities import tile_raster_images
from spynet.utils.utilities import load_config
from data_brain_parcellation import generate_and_save
class ExperimentBrain(Experiment):
"""
Experiment in which the network is iteratively trained on its misclassified datapoints. kind of boosting.
"""
def __init__(self, exp_name, data_path):
Experiment.__init__(self, exp_name, data_path)
def copy_file_virtual(self):
copy2(inspect.getfile(inspect.currentframe()), self.path)
def run(self):
###### Create the datasets
# Testing
data_cf_test = load_config("cfg_testing_data_creation.py")
data_cf_test.general["file_path"] = data_cf_test.data_path + "test_temp.h5"
data_generator_test = DataGeneratorBrain()
data_generator_test.init_from_config(data_cf_test)
ds_test_temp = DatasetBrainParcellation()
vx, inputs, outputs, file_ids = data_generator_test.generate_parallel(data_cf_test.general["n_data"])
ds_test_temp.populate(inputs, outputs, vx, file_ids)
ds_test_temp.shuffle_data()
# Training
prop_validation = 0.1
data_cf_train = load_config("cfg_training_data_creation.py")
data_cf_train.general["file_path"] = data_cf_train.data_path + "train_temp.h5"
data_generator_train = DataGeneratorBrain()
data_generator_train.init_from_config(data_cf_train)
ds_train_temp = DatasetBrainParcellation()
vx, inputs, outputs, file_ids = data_generator_train.generate_parallel(data_cf_train.general["n_data"])
ds_train_temp.populate(inputs, outputs, vx, file_ids)
ds_train_temp.shuffle_data()
[ds_train_temp, ds_validation_temp] = ds_train_temp.split_dataset_proportions([1-prop_validation, prop_validation])
## Scale some part of the data
print "Scaling"
s = Scaler([slice(-134, None, None)])
s.compute_parameters(ds_train_temp.inputs)
s.scale(ds_train_temp.inputs)
s.scale(ds_validation_temp.inputs)
s.scale(ds_test_temp.inputs)
pickle.dump(s, open(self.path + "s.scaler", "wb"))
###### Create the network
net = NetworkUltimateConv()
net.init(29, 29, 13, 134, 135)
print net
###### Configure the trainer
# Cost function
cost_function = CostNegLL()
# Learning update
learning_rate = 0.05
momentum = 0.5
lr_update = LearningUpdateGDMomentum(learning_rate, momentum)
# Create monitors and add them to the trainer
freq = 1
freq2 = 0.00001
err_training = MonitorErrorRate(freq, "Train", ds_train_temp)
err_testing = MonitorErrorRate(freq, "Test", ds_test_temp)
err_validation = MonitorErrorRate(freq, "Val", ds_validation_temp)
# dice_training = MonitorDiceCoefficient(freq, "Train", ds_train_temp, 135)
dice_testing = MonitorDiceCoefficient(freq, "Test", ds_test_temp, 135)
# dice_validation = MonitorDiceCoefficient(freq, "Val", ds_validation_temp, 135)
# Create stopping criteria and add them to the trainer
max_epoch = MaxEpoch(100)
#early_stopping = EarlyStopping(err_validation, 10, 0.99, 5)
# Create the network selector
params_selector = ParamSelectorBestMonitoredValue(err_validation)
# Create the trainer object
batch_size = 200
t = Trainer(net, cost_function, params_selector, [max_epoch],
lr_update, ds_train_temp, batch_size,
[err_training, err_validation, err_testing, dice_testing])
###### Train the network
n_iter = 1
evolution = np.zeros((2, n_iter))
n_validation_data = ds_validation_temp.n_data
for i in xrange(n_iter):
# Train
t.train()
net.save_parameters(self.path + "net.net")
# Test
print "ERRORS::::"
out_pred = net.predict(ds_test_temp.inputs, 1000)
errors = np.argmax(out_pred, axis=1) != np.argmax(ds_test_temp.outputs, axis=1)
print np.mean(errors)
evolution[0,i] = np.mean(errors)
out_pred = net.predict(ds_validation_temp.inputs, 1000)
errors = np.argmax(out_pred, axis=1) != np.argmax(ds_validation_temp.outputs, axis=1)
print np.mean(errors)
evolution[1,i] = np.mean(errors)
vx_errors = ds_train_temp.vx[errors]
file_ids_errors = ds_validation_temp.file_ids[errors]
inputs_errors = ds_validation_temp.inputs[errors]
outputs_errors = ds_validation_temp.outputs[errors]
# Update datasets, trainer, monitors
n_data = int(round(0.9*data_cf_train.general["n_data"]))
prop_validation = 0.1
vx, inputs, outputs, file_ids = data_generator_train.generate_parallel(n_data)
s.scale(inputs)
split = int(round(n_data*(1-prop_validation)))
ds_train_temp.populate(np.concatenate([inputs[0:split], inputs_errors], axis=0),
np.concatenate([outputs[0:split], outputs_errors], axis=0),
np.concatenate([vx[0:split], vx_errors], axis=0),
np.concatenate([file_ids[0:split], file_ids_errors], axis=0))
ds_train_temp.shuffle_data()
ds_validation_temp.populate(inputs[split:n_data], outputs[split:n_data], vx[split:n_data], file_ids[split:n_data])
ds_validation_temp.shuffle_data()
print ds_train_temp.n_data
t.init()
print evolution
###### Plot the records
# save_records_plot(self.path, [err_training, err_validation, err_testing], "err", t.n_train_batches, "upper right")
# save_records_plot(self.path, [dice_testing], "dice", t.n_train_batches, "lower right")
###### Save the network
net.save_parameters(self.path + "net.net")
if __name__ == '__main__':
exp_name = "test_iter"
data_path = "./datasets/test_iter_f1/"
exp = ExperimentBrain(exp_name, data_path)
exp.run()
