def train3D(self):
model = self.createModel3D([128,128,48])
print('-'*30)
print('Loading training data...')
print('-'*30)
train_images_path, train_labels_path = self.arrangeDataPath(self.root_folder, self.image_folder,self.mask_folder)
hist={};hist['acc']=[];hist['loss']=[]
for epochs in range(self.numEpochs):
print('epochs: ', epochs)
acc=0;loss=0
for each in os.listdir(train_images_path):
print('case: ', each)
trainImages, trainLabels,affine = self.load3DtrainingData(train_images_path,train_labels_path, each)
trainImages = interp3D(trainImages,[0.25,0.25,1],cval=-1024)
trainLabels = interp3D(trainLabels,[0.25,0.25,1],cval=0)
trainImages,trainLabels = arrange3Ddata(trainImages,trainLabels,48,self.dtype)
[numImgs,img_rows,img_cols,img_dep,ch] = trainImages.shape
print('training image shape:',trainImages.shape)
trainLabels=trainLabels.reshape((numImgs,img_rows*img_cols*img_dep))
if self.wType=='slice':
wImg = self.sliceBasedWeighting3D(trainLabels)
else:
# wImg = self.volumeBasedWeighting(trainLabels)
wImg=np.ones(trainLabels.shape)
trainLabels = np_utils.to_categorical(trainLabels, self.nb_classes)
trainLabels = trainLabels.reshape((numImgs,img_rows*img_cols*img_dep,1,self.nb_classes))
trainLabels = trainLabels.astype(self.dtype)
print('-'*30)
print('Training model...')
print('-'*30)
history=model.fit(trainImages, trainLabels, batch_size=self.bs, epochs=1, verbose=1,sample_weight=wImg)
acc = acc+history.history['acc'][0]
loss=loss+history.history['loss'][0]
if ((epochs>0) and ((epochs+1)%25)==0):
model.save_weights(os.path.join(self.save_folder,str(epochs+1)+'_'+self.checkWeightFileName))
# model.save_weights(os.path.join(self.save_folder,str(epochs+1)+'_'+self.checkWeightFileName))
hist['acc'].append(acc/len(os.listdir(train_images_path)))
hist['loss'].append(loss/len(os.listdir(train_images_path)))
np.save(self.save_folder+'history.npy',hist)
return
def Predict3D(self, weights):
test_images_path, test_labels_path = self.arrangeDataPath(
self.root_folder, self.image_folder, self.mask_folder)
print('-' * 30)
print('Loading saved weights...')
print('-' * 30)
model = self.createModel3D([128, 128, 48])
model.load_weights(os.path.join(self.save_folder, weights))
from datetime import datetime
startTime = datetime.now()
for each in os.listdir(test_images_path):
print('case: ', each)
startTime = datetime.now()
testImages, otestLabels, affine = self.load3DtrainingData(
test_images_path, test_labels_path, each)
oNumImgs = testImages.shape[2]
testImages = interp3D(testImages, [0.25, 0.25, 1], cval=-1024)
testLabels = interp3D(otestLabels, [0.25, 0.25, 1], cval=0)
testImages, testLabels = arrange3Ddata(testImages, testLabels, 48,
self.dtype)
[numImgs, img_rows, img_cols, img_dep, ch] = testImages.shape
print('training image shape:', testImages.shape)
testLabels = testLabels.reshape(
(numImgs, img_rows * img_cols * img_dep))
testLabels = np_utils.to_categorical(testLabels, self.nb_classes)
testLabels = testLabels.reshape(
(numImgs, img_rows * img_cols * img_dep, 1, self.nb_classes))
testLabels = testLabels.astype(self.dtype)
predImage = model.predict(testImages, batch_size=1, verbose=1)
print('-' * 30)
print('Predicting masks on test data...')
print('-' * 30)
#for comuting metrics of hyper dense class
predImage = predImage.reshape(
(numImgs, img_rows, img_cols, img_dep,
self.nb_classes))[:, :, :, :, self.sC - 1:self.sC]
print(predImage.shape)
predImage = predImage[0, :, :, :, 0]
predImage = interp3D(predImage, [4, 4, 1], cval=0)
predImage = (predImage > 0.5).astype(self.dtype)
print('test labels shape: ', predImage.shape)
print(datetime.now() - startTime)
saveFolder = os.path.join(self.save_folder, self.pred_folder)
testLabels = testLabels.reshape(
(numImgs, img_rows, img_cols, img_dep,
self.nb_classes))[0, :, :, :, self.sC - 1:self.sC]
if self.testLabelFlag:
self.computeTestMetrics(otestLabels, predImage)
if self.testMetricFlag:
self.saveTestMetrics(saveFolder, otestLabels, predImage, each)
if self.savePredMask:
predImage = predImage.astype('uint8')
predImage = nb.Nifti1Image(
predImage.reshape(512, 512, img_dep), affine)
nb.save(predImage, saveFolder + '/' + each)
return