# ==============================================================================

# 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

from keras.models import Sequential,Model

from keras.optimizers import SGD, Adam

from keras.layers import Input#, Dropout, Dense, Conv3D, MaxPooling3D, GlobalMaxPooling3D ,GlobalAveragePooling3D, Activation, BatchNormalization,Flatten

#from keras.models import Sequential

#from keras.layers import Dropout, Dense, Conv3D, MaxPooling3D,GlobalMaxPooling3D, GlobalAveragePooling3D, Activation, BatchNormalization,Flatten

from resnet3d import Resnet3DBuilder

#from keras.regularizers import l2

def generate_model(learning_rate = 1 * 10 **(-4)):

    print('**************************')

    model = Resnet3DBuilder.build_resnet_18((48, 96, 18, 32), 1)

    model.compile(loss='binary_crossentropy',

                      metrics = ['accuracy'],

                      optimizer = Adam(lr=learning_rate,beta_1=0.99, beta_2=0.999))#SGD(lr=1e-2, momentum = 0.9))

    return model

    #input1 = Input(shape=(384, 384, 36, 1))

    #input2 = Input(shape=(352, 352, 144, 1))

    #out = Resnet3DBuilder.build_resnet_18((48, 96, 18, 32),input1,input2,1)

    #merged_model = Model([input1, input2], out)

    merged_model.compile(loss='binary_crossentropy',

                      metrics = ['accuracy'],

                      optimizer = Adam(lr=learning_rate,beta_1=0.99, beta_2=0.999))#SGD(lr=1e-2, momentum = 0.9))

    #loss='categorical_crossentropy'

    return merged_model