# ==============================================================================
# Copyright (C) 2023 Haresh Rengaraj Rajamohan, Tianyu Wang, Kevin Leung,
# Gregory Chang, Kyunghyun Cho, Richard Kijowski & Cem M. Deniz
#
# This file is part of OAI-MRI-TKR
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
# ==============================================================================
#!/usr/bin/env python3
import h5py
import os.path
import numpy as np
import pandas as pd
import math
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import tensorflow as tf
#from sklearn.model_selection import StratifiedKFold
from ModelResnet3D import generate_model
from DataGenerator import DataGenerator
#from keras.models import Sequential
#from keras.optimizers import SGD, Adam
#from keras.layers import Dropout, Dense, Conv3D, MaxPooling3D, GlobalAveragePooling3D, Activation, BatchNormalization,Flatten
from keras.callbacks import LearningRateScheduler, TensorBoard, EarlyStopping, ModelCheckpoint, Callback
from sklearn.metrics import roc_auc_score
tf.app.flags.DEFINE_boolean('batch_norm', True, 'Use BN or not')
tf.app.flags.DEFINE_float('lr', 0.0001, 'Initial learning rate.')
tf.app.flags.DEFINE_integer('filters_in_last', 128, 'Number of filters on the last layer')
tf.app.flags.DEFINE_float('dr',0.0, 'Dropout rate when training.')
tf.app.flags.DEFINE_string('input_file','/gpfs/data/denizlab/Datasets/OAI/SAG_IW_TSE/dataBefore201807/data_SAG_IW_TSE.hdf5', 'File to read data')
tf.app.flags.DEFINE_string('file_path', '/gpfs/data/denizlab/Users/hrr288/Radiology_test/', 'Main Folder to Save outputs')
tf.app.flags.DEFINE_integer('val_fold', 1, 'Fold for cv')
tf.app.flags.DEFINE_string('file_folder','/gpfs/data/denizlab/Datasets/OAI/SAG_IW_TSE/', 'Path to HDF5 radiographs')
tf.app.flags.DEFINE_string('csv_path', '/gpfs/data/denizlab/Users/hrr288/TSE_dataset/', 'Folder with the fold splits')
FLAGS = tf.app.flags.FLAGS
class auc_Histories(Callback):
def on_train_begin(self, logs={}):
self.aucs = []
self.losses = []
def on_train_end(self, logs={}):
return
def on_epoch_begin(self, epoch, logs={}):
return
def on_epoch_end(self, epoch, logs={}):
self.losses.append(logs.get('loss'))
y_pred = self.model.predict(self.model.validation_data)
self.aucs.append(roc_auc_score(self.model.validation_data[1], y_pred))
return
def on_batch_begin(self, batch, logs={}):
return
def on_batch_end(self, batch, logs={}):
return
class roc_callback(Callback):
def __init__(self,index,val_fold):
_params = {'dim': (384,384,36),
'batch_size': 4,
'n_classes': 2,
'n_channels': 1,
'shuffle': False,
'normalize' : True,
'randomCrop' : False,
'randomFlip' : False,
'flipProbability' : -1}
self.x = DataGenerator(directory = FLAGS.csv_path+'Fold_'+str(val_fold)+'/CV_'+str(index)+'_train.csv',file_folder=FLAGS.file_folder, **_params)
self.x_val = DataGenerator(directory = FLAGS.csv_path+'Fold_'+str(val_fold)+'/CV_'+str(index)+'_val.csv',file_folder=FLAGS.file_folder, **_params)
self.y = pd.read_csv(FLAGS.csv_path+'Fold_'+str(val_fold)+'/CV_'+str(index)+'_train.csv').Label
self.y_val = pd.read_csv(FLAGS.csv_path+'Fold_'+str(val_fold)+'/CV_'+str(index)+'_val.csv').Label
self.auc = []
self.val_auc = []
self.losses = []
self.val_losses = []
def on_train_begin(self, logs={}):
return
def on_train_end(self, logs={}):
return
def on_epoch_begin(self, epoch, logs={}):
return
def on_epoch_end(self, epoch, logs={}):
self.losses.append(logs.get('loss'))
self.val_losses.append(logs.get('val_loss'))
y_pred = self.model.predict_generator(self.x)
y_true = self.y[:len(y_pred)]
roc = roc_auc_score(y_true, y_pred)
y_pred_val = self.model.predict_generator(self.x_val)
y_true_val = self.y_val[:len(y_pred_val)]
roc_val = roc_auc_score(y_true_val, y_pred_val)
self.auc.append(roc)
self.val_auc.append(roc_val)
#print(len(y_true),len(y_true_val))
print('\rroc-auc: %s - roc-auc_val: %s' % (str(round(roc,4)),str(round(roc_val,4))),end=100*' '+'\n')
return
def on_batch_begin(self, batch, logs={}):
return
def on_batch_end(self, batch, logs={}):
return
'''
Def: Code to plot loss curves
Params: history = keras output from training
loss_path = path to save curve
'''
def plot_loss_curves(history, loss_path): #, i):
f = plt.figure()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
#plt.show()
#path = '/data/kl2596/curves/loss/' + loss_path + '.jpeg'
f.savefig(loss_path)
'''
Def: Code to plot accuracy curves
Params: history = keras output from training
acc_path = path to save curve
'''
def plot_accuracy_curves(history, acc_path): #, i):
f = plt.figure()
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('acc')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
#plt.show()
#path = '/data/kl2596/curves/accuracy/' + acc_path + '.jpeg'
f.savefig(acc_path)
def plot_auc_curves(auc_history, acc_path): #, i):
f = plt.figure()
plt.plot(auc_history.auc)
plt.plot(auc_history.val_auc)
plt.title('model AUC')
plt.ylabel('auc')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
#plt.show()
#path = '/data/kl2596/curves/accuracy/' + acc_path + '.jpeg'
f.savefig(acc_path)
def train_model(model, train_data, val_data, path, index,val_fold):
#model.summary()
# Early Stopping callback that can be found on Keras website
# Create path to save weights with model checkpoint
weights_path = path + 'weights-{epoch:02d}-{val_loss:.2f}-{val_acc:.2f}-{loss:.2f}-{acc:.2f}.hdf5'
model_checkpoint = ModelCheckpoint(weights_path, monitor = 'val_loss', save_best_only = True,
verbose=0, period=1)
# Save loss and accuracy curves using Tensorboard
tensorboard_callback = TensorBoard(log_dir = path,
histogram_freq = 0,
write_graph = False,
write_grads = False,
write_images = False)
auc_history = roc_callback(index,val_fold)
#es = EarlyStopping(monitor='val_auc', mode='max', verbose=1, patience=50)
callbacks_list = [model_checkpoint, tensorboard_callback, auc_history]
#es = EarlyStopping(monitor='val_auc', mode='max', verbose=1, patience=150)
history = model.fit_generator(generator = train_data, validation_data = val_data, epochs=10,
#use_multiprocessing=True, workers=6,
callbacks = callbacks_list)
accuracy = auc_history.val_auc
print('*****************************')
print('best auc:',np.max(accuracy))
print('average auc:',np.mean(accuracy))
print('*****************************')
accuracy = history.history['val_acc']
print('*****************************')
print('best acc:',np.max(accuracy))
print('average acc:',np.mean(accuracy))
print('*****************************')
loss_path = path + 'loss_curve.jpeg'
acc_path = path + 'acc_curve.jpeg'
auc_path = path + 'auc_curve.jpeg'
plot_loss_curves(history, loss_path)
plot_accuracy_curves(history, acc_path)
plot_auc_curves(auc_history, auc_path)
#model.save_weights(weights_path)
'''
Def: Code to run stratified cross validation to train my network
Params: num_of_folds = number of folds to cross validate
lr = learning rate
dr = dropout rate
filters_in_last = number of filters in last convolutional layer (we tested 64 and 128)
batch_norm = True or False for batch norm in model
data = MRI images
labels = labels corresponding to MRI images
file_path = path to save network weights, curves, and tensorboard callbacks
'''
def cross_validation(val_fold, lr, dr, filters_in_last, file_path):
train_params = {'dim': (384,384,36),
'batch_size': 4,
'n_classes': 2,
'n_channels': 1,
'shuffle': True,
'normalize' : True,
'randomCrop' : True,
'randomFlip' : True,
'flipProbability' : -1}
val_params = {'dim': (384,384,36),
'batch_size': 4,
'n_classes': 2,
'n_channels': 1,
'shuffle': False,
'normalize' : True,
'randomCrop' : False,
'randomFlip' : False,
'flipProbability' : -1}
model_path = file_path + 'Tnetres_Best/lr24ch32kerne773773_strde222_new_arch/'
if not os.path.exists(model_path):
os.makedirs(model_path)
num_of_folds = 6
for i in range(num_of_folds):
model = generate_model(learning_rate = 2 * 10 **(-4))
model.summary()
print(train_params)
#print(train_index, test_index)
print('Running Fold', i+1, '/', num_of_folds)
fold_path = model_path + 'Fold_' + str(val_fold) + '/CV_'+str(i+1)+'/'
print(fold_path)
if not os.path.exists(fold_path):
os.makedirs(fold_path)
training_generator = DataGenerator(directory = FLAGS.csv_path+'Fold_'+str(val_fold)+'/CV_'+str(i+1)+'_train.csv',file_folder=FLAGS.file_folder, **train_params)
validation_generator = DataGenerator(directory = FLAGS.csv_path+'Fold_'+str(val_fold)+'/CV_'+str(i+1)+'_val.csv',file_folder=FLAGS.file_folder, **val_params)
train_model(model=model,
train_data = training_generator,
val_data = validation_generator,
path = fold_path, index = i+1,val_fold=val_fold)
def main(argv=None):
print('Begin training for fold ',FLAGS.val_fold)
cross_validation(val_fold=FLAGS.val_fold,
lr=FLAGS.lr, dr=FLAGS.dr, filters_in_last=FLAGS.filters_in_last,
file_path = FLAGS.file_path)
if __name__ == "__main__":
tf.app.run()
Datasets
Models
