import itertools
import math
import os
from functools import partial
import keras
from keras import backend as K
from keras.callbacks import ModelCheckpoint, CSVLogger, LearningRateScheduler, ReduceLROnPlateau, EarlyStopping, \
LambdaCallback
from keras.models import load_model, Model
import fetal_net.model
from fetal_net.metrics import (dice_coefficient, dice_coefficient_loss, dice_coef, dice_coef_loss,
weighted_dice_coefficient_loss, weighted_dice_coefficient,
vod_coefficient, vod_coefficient_loss, focal_loss, dice_and_xent, double_dice_loss)
K.set_image_dim_ordering('th')
from multiprocessing import cpu_count
# learning rate schedule
def step_decay(epoch, initial_lrate, drop, epochs_drop):
return initial_lrate * math.pow(drop, math.floor((1 + epoch) / float(epochs_drop)))
def get_callbacks(model_file, initial_learning_rate=0.0001, learning_rate_drop=0.5, learning_rate_epochs=None,
learning_rate_patience=50, logging_file="training.log", verbosity=1,
early_stopping_patience=None):
callbacks = list()
callbacks.append(
ModelCheckpoint(model_file + '-epoch{epoch:02d}-loss{val_loss:.3f}-acc{val_binary_accuracy:.3f}.h5',
save_best_only=True, verbose=verbosity, monitor='val_loss'))
callbacks.append(CSVLogger(logging_file, append=True))
if learning_rate_epochs:
callbacks.append(LearningRateScheduler(partial(step_decay, initial_lrate=initial_learning_rate,
drop=learning_rate_drop, epochs_drop=learning_rate_epochs)))
else:
callbacks.append(ReduceLROnPlateau(factor=learning_rate_drop, patience=learning_rate_patience,
verbose=verbosity))
if early_stopping_patience:
callbacks.append(EarlyStopping(verbose=verbosity, patience=early_stopping_patience))
return callbacks
def load_old_model(model_file, verbose=True, config=None) -> Model:
print("Loading pre-trained model")
custom_objects = {'dice_coefficient_loss': dice_coefficient_loss, 'dice_coefficient': dice_coefficient,
'dice_coef': dice_coef, 'dice_coef_loss': dice_coef_loss,
'weighted_dice_coefficient': weighted_dice_coefficient,
'weighted_dice_coefficient_loss': weighted_dice_coefficient_loss,
'vod_coefficient': vod_coefficient,
'vod_coefficient_loss': vod_coefficient_loss,
'focal_loss': focal_loss,
'focal_loss_fixed': focal_loss,
'dice_and_xent': dice_and_xent,
'double_dice_loss': double_dice_loss }
try:
from keras_contrib.layers import InstanceNormalization
custom_objects["InstanceNormalization"] = InstanceNormalization
except ImportError:
pass
try:
if verbose:
print('Loading model from {}...'.format(model_file))
return load_model(model_file, custom_objects=custom_objects)
except ValueError as error:
print(error)
if 'InstanceNormalization' in str(error):
raise ValueError(str(error) + "\n\nPlease install keras-contrib to use InstanceNormalization:\n"
"'pip install git+https://www.github.com/keras-team/keras-contrib.git'")
else:
if config is not None:
print('Trying to build model manually...')
loss_func = getattr(fetal_net.metrics, config['loss'])
model_func = getattr(fetal_net.model, config['model_name'])
model = model_func(input_shape=config["input_shape"],
initial_learning_rate=config["initial_learning_rate"],
**{'dropout_rate': config['dropout_rate'],
'loss_function': loss_func,
'mask_shape': None if config["weight_mask"] is None else config["input_shape"],
# TODO: change to output shape
'old_model_path': config['old_model']})
model.load_weights(model_file)
return model
else:
raise
def train_model(model, model_file, training_generator, validation_generator, steps_per_epoch, validation_steps,
initial_learning_rate=0.001, learning_rate_drop=0.5, learning_rate_epochs=None, n_epochs=500,
learning_rate_patience=20, early_stopping_patience=None, output_folder='.'):
"""
Train a Keras model.
:param early_stopping_patience: If set, training will end early if the validation loss does not improve after the
specified number of epochs.
:param learning_rate_patience: If learning_rate_epochs is not set, the learning rate will decrease if the validation
loss does not improve after the specified number of epochs. (default is 20)
:param model: Keras model that will be trained.
:param model_file: Where to save the Keras model.
:param training_generator: Generator that iterates through the training data.
:param validation_generator: Generator that iterates through the validation data.
:param steps_per_epoch: Number of batches that the training generator will provide during a given epoch.
:param validation_steps: Number of batches that the validation generator will provide during a given epoch.
:param initial_learning_rate: Learning rate at the beginning of training.
:param learning_rate_drop: How much at which to the learning rate will decay.
:param learning_rate_epochs: Number of epochs after which the learning rate will drop.
:param n_epochs: Total number of epochs to train the model.
:return:
"""
model.fit_generator(generator=training_generator,
steps_per_epoch=steps_per_epoch,
epochs=n_epochs,
validation_data=validation_generator,
validation_steps=validation_steps,
max_queue_size=15,
workers=1,
use_multiprocessing=False,
callbacks=get_callbacks(model_file,
initial_learning_rate=initial_learning_rate,
learning_rate_drop=learning_rate_drop,
learning_rate_epochs=learning_rate_epochs,
learning_rate_patience=learning_rate_patience,
early_stopping_patience=early_stopping_patience,
logging_file=os.path.join(output_folder, 'training')))
