def train_leave_one_out(tempStore, modelPath, testOutputDir, Reference):
print('-'*30)
print('Loading all the data...')
print('-'*30)
imgs_train, imgs_label_train, addInformation_train, imgs_id_train = load_train_data(tempStore)
imgs_train = preprocess(imgs_train)
imgs_label_train = preprocess(imgs_label_train)
addInformation_train = preprocess(addInformation_train)
imgs_train = imgs_train.astype('float32')
if IfglobalNorm == True:
mean = np.mean(imgs_train) # mean for data centering
std = np.std(imgs_train) # std for data normalization
imgs_train -= mean
imgs_train /= std
# save mean and std of training data
imgs_label_train = imgs_label_train.astype(np.uint32)
addInformation_train = addInformation_train.astype(np.float32)
TotalNum = len(imgs_id_train)
preImageList = []
for i in xrange(TotalNum):
inBaseName = os.path.basename(imgs_id_train[i])
outBaseName = string.join(inBaseName.split("_")[-4:-1], "_")
currentTrainImgs = np.delete(imgs_train,range(i*sliceNum,(i+1)*sliceNum), axis=0)
currentTrainLab = np.delete(imgs_label_train,range(i*sliceNum,(i+1)*sliceNum), axis=0)
currentTrainAdd = np.delete(addInformation_train,range(i*sliceNum,(i+1)*sliceNum), axis=0)
# begin the model
#############################
model = get_unet_short()
#############################
weightName = modelPath + '/' + outBaseName + '_weights.h5'
model_checkpoint = ModelCheckpoint(weightName, monitor='val_loss', save_best_only=True)
early_stop = EarlyStopping(monitor='val_loss', min_delta=0, patience=40, verbose=0, mode='auto')
train_history = model.fit([currentTrainImgs, currentTrainAdd], currentTrainLab, batch_size,\
epochs, verbose=1, shuffle=True, validation_split=0.2,\
callbacks=[model_checkpoint, early_stop])
loss = train_history.history['loss']
val_loss = train_history.history['val_loss']
np.save(tempStore + '/' + outBaseName + '_loss.npy',loss)
np.save(tempStore + '/' + outBaseName + '_val_loss',val_loss)
# prediction
currentTestImgs = imgs_train[i*sliceNum:(i+1)*sliceNum,:,:,:]
currentTestAdd = addInformation_train[i*sliceNum:(i+1)*sliceNum,:,:,:]
model.load_weights(weightName)
imgs_label_test = model.predict([currentTestImgs,currentTestAdd], verbose=1)
ThreeDImagePath = VolumeDataTofiles(imgs_label_test, outBaseName, testOutputDir, Reference)
preImageList.append(ThreeDImagePath)
# np.save(tempStore + '/' + outBaseName + '_imgs_label_test.npy', imgs_label_test)
print('-'*30)
print(str(i) + 'th is finished...')
print('-'*30)
WriteListtoFile(preImageList, testOutputDir + '/FileList.txt')
def train_and_predict(tempStore, modelPath):
print('-' * 30)
print('Loading and preprocessing train data...')
print('-' * 30)
imgs_train, imgs_label_train, imgs_id_train = load_train_data(tempStore)
imgs_train = preprocess(imgs_train)
imgs_label_train = preprocess(imgs_label_train)
imgs_train = imgs_train.astype('float32')
if IfglobalNorm == True:
mean = np.mean(imgs_train) # mean for data centering
std = np.std(imgs_train) # std for data normalization
imgs_train -= mean
imgs_train /= std
# save mean and std of training data
imgs_label_train = imgs_label_train.astype(np.uint32)
print('-' * 30)
print('Creating and compiling model...')
print('-' * 30)
##############################
model = get_unet_short()
##############################
model_checkpoint = ModelCheckpoint(os.path.join(modelPath, 'weights.h5'),
monitor='val_loss',
save_best_only=True)
early_stop = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=40,
verbose=0,
mode='auto')
#
print('-' * 30)
print('Fitting model...')
print('-' * 30)
train_history = model.fit([imgs_train], imgs_label_train, batch_size,\
epochs, verbose=1, shuffle=True, validation_split=0.2,\
callbacks=[model_checkpoint, early_stop])
loss = train_history.history['loss']
val_loss = train_history.history['val_loss']
np.save(tempStore + '/loss.npy', loss)
np.save(tempStore + '/val_loss', val_loss)
print('-' * 30)
print('Loading and preprocessing test data...')
print('-' * 30)
imgs_test, imgs_id_test = load_test_data(tempStore)
imgs_test = preprocess(imgs_test)
imgs_test = imgs_test.astype('float32')
if IfglobalNorm == True:
imgs_test -= mean
imgs_test /= std
print('-' * 30)
print('Loading saved weights...')
print('-' * 30)
model.load_weights(os.path.join(modelPath, 'weights.h5'))
print('-' * 30)
print('Predicting masks on test data...')
print('-' * 30)
imgs_label_test = model.predict([imgs_test], verbose=1)
np.save(os.path.join(tempStore, 'imgs_label_test.npy'), imgs_label_test)
if IfglobalNorm == True:
np.save(os.path.join(tempStore, 'imgs_mean.npy'), mean)
np.save(os.path.join(tempStore, 'imgs_std.npy'), std)