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Train_ablation.py
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Train_ablation.py
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from __future__ import division, print_function
from keras.layers import Add,Multiply,pooling,Conv2D, Activation, Concatenate, concatenate, MaxPooling2D, ZeroPadding2D,Conv2DTranspose, Cropping2D, average, Input,normalization
from keras.optimizers import Adam,SGD
from keras import Model
from keras.layers import Dropout, Lambda
from keras.callbacks import ModelCheckpoint, EarlyStopping, CSVLogger, ReduceLROnPlateau
import keras.backend.tensorflow_backend as KTF
import tensorflow as tf
import numpy as np
from keras import backend as K
from Loss_function import *
from keras.utils import plot_model
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
KTF.set_session(tf.Session(config=tf.ConfigProto(device_count={'gpu': 0})))
K.set_image_data_format('channels_last')
flt = 44
def dial_multi_conv(flt, input):
conv1 = Conv2D(flt, (3, 3), dilation_rate=1, padding='same')(input)
conv1 = normalization.BatchNormalization()(conv1)
conv1 = Activation('relu')(conv1)
conv2 = Conv2D(flt, (3, 3), dilation_rate=2, padding='same')(input)
conv2 = normalization.BatchNormalization()(conv2)
conv2 = Activation('relu')(conv2)
conv3 = Conv2D(flt, (3, 3), dilation_rate=4, padding='same')(input)
conv3 = normalization.BatchNormalization()(conv3)
conv3 = Activation('relu')(conv3)
concat = concatenate([conv1,conv2,conv3],axis=3)
concat = Conv2D(flt, (3,3),padding='same')(concat)
concat = normalization.BatchNormalization()(concat)
concat = Activation('relu')(concat)
return concat
def IB(input,flt):
conv1 = Conv2D(flt, (1, 1), activation='relu', padding='same')(input)
conv2 = Conv2D(flt*2, (3, 3), activation='relu', padding='same')(input)
conv2 = Conv2D(flt, (1,1), activation='relu', padding='same')(conv2)
conv3 = Conv2D(flt*2, (5, 5), activation='relu', padding='same')(input)
conv3 = Conv2D(flt, (1, 1), activation='relu', padding='same')(conv3)
concate = concatenate([conv1, conv2, conv3], axis=3)
conv = Conv2D(flt, (1, 1), activation='relu')(concate)
output = conv
return output
def conv_bn_relu(flt, input):
kwargs = dict(kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')
conv1 = Conv2D(flt, **kwargs)(input)
conv1 = normalization.BatchNormalization()(conv1)
conv1 = Activation('relu')(conv1)
return conv1
def path1(x = Input(shape=(256, 256, 1)), features=16, depth=4):
inputs = x
maps = [inputs]
#ib = IB(inputs, features)
x = Conv2D(features, kernel_size=(3, 3), activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
for n in range(depth):
x = Conv2D(features, (3, 3), strides=1, dilation_rate=2, padding='same',kernel_initializer='he_normal')(x)
maps.append(x)
x = Concatenate(axis=3)(maps)
x = normalization.BatchNormalization()(x)
x = Activation('relu')(x)
x1 = Conv2D(features, kernel_size=(3, 3), activation='relu', padding='same', kernel_initializer='he_normal')(x)
return x1
def Cardiac_Seg1(x = Input(shape=(256, 256, 1))):
inputs = x
# inputs = IB(inputs, flt)
global conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8, conv9, conv10, conv11, conv12, conv13, conv14, conv15, pool1, pool2, pool3, pool4
conv1 = conv_bn_relu(flt, inputs)
conv2 = conv_bn_relu(flt, conv1)
pool1 = MaxPooling2D(pool_size=2, strides=2, padding='same')(conv2) # 128
conv4 = conv_bn_relu(flt * 2, pool1)
conv5 = conv_bn_relu(flt * 2, conv4)
pool2 = MaxPooling2D(pool_size=2, strides=2, padding='same')(conv5) # 64
conv7 = conv_bn_relu(flt * 4, pool2)
conv8 = conv_bn_relu(flt * 4, conv7)
pool3 = MaxPooling2D(pool_size=2, strides=2, padding='same')(conv8) # 32
conv10 = conv_bn_relu(flt * 8, pool3)
conv11 = conv_bn_relu(flt * 8, conv10)
pool4 = MaxPooling2D(pool_size=2, strides=2, padding='same')(conv11) # 16
conv13 = conv_bn_relu(flt * 16, pool4)
conv14 = conv_bn_relu(flt * 16, conv13)
conv14 = Dropout(0.5)(conv14)
return conv14
def Cardiac_Seg2(x = Input(shape=(256, 256, 1))):
inputs = x
#inputs = IB(inputs, flt)
global co12, co22, co32, co42, co52, co62, co72, co82, co92, co102, co112, co122, co132, co142, co152, po12, po22, po32, po42
co12 = conv_bn_relu(flt, inputs)
co22 = conv_bn_relu(flt, co12)
po12 = MaxPooling2D(pool_size=2, strides=2, padding='same')(co22) # 128
co42 = conv_bn_relu(flt * 2, po12)
co52 = conv_bn_relu(flt * 2, co42)
po22 = MaxPooling2D(pool_size=2, strides=2, padding='same')(co52) # 64
co72 = conv_bn_relu(flt * 4, po22)
co82 = conv_bn_relu(flt * 4, co72)
po32 = MaxPooling2D(pool_size=2, strides=2, padding='same')(co82) # 32
co102 = conv_bn_relu(flt * 8, po32)
co112 = conv_bn_relu(flt * 8, co102)
po42 = MaxPooling2D(pool_size=2, strides=2, padding='same')(co112) # 16
co132 = conv_bn_relu(flt * 16, po42)
co142 = conv_bn_relu(flt * 16, co132)
co142 = Dropout(0.5)(co142)
return co142
def Cardiac_Seg3(x = Input(shape=(256, 256, 1))):
inputs = x
#inputs = IB(inputs, flt)
global con13, con23, con33, con43, con53, con63, con73, con83, con93, con103, con113, con123, con133, con143, con153, poo13, poo23, poo33, poo43
con13 = conv_bn_relu(flt, inputs)
con23 = conv_bn_relu(flt, con13)
poo13 = MaxPooling2D(pool_size=2, strides=2, padding='same')(con23) # 128
con43 = conv_bn_relu(flt * 2, poo13)
con53 = conv_bn_relu(flt * 2, con43)
poo23 = MaxPooling2D(pool_size=2, strides=2, padding='same')(con53) # 64
con73 = conv_bn_relu(flt * 4, poo23)
con83 = conv_bn_relu(flt * 4, con73)
poo33 = MaxPooling2D(pool_size=2, strides=2, padding='same')(con83) # 32
con103 = conv_bn_relu(flt * 8, poo33)
con113 = conv_bn_relu(flt * 8, con103)
poo43 = MaxPooling2D(pool_size=2, strides=2, padding='same')(con113) # 16
con133 = conv_bn_relu(flt * 16, poo43)
con143 = conv_bn_relu(flt * 16, con133)
con143 = Dropout(0.5)(con143)
return con143
def MLM(a = Input(shape=(256, 256, 1)), b = Input(shape=(256, 256, 1)), c = Input(shape=(256, 256, 1)),num_classes=6):
kwargs = dict(filters=num_classes, kernel_size=(3, 3), strides=2, padding='same', kernel_initializer='he_normal')
model1 = Cardiac_Seg1(a)
model2 = Cardiac_Seg2(b)
model3 = Cardiac_Seg3(c)
merge1 = concatenate([model1, model2, model3], axis=-1) # (?, 16, 16, 512*3)
merge1 = Conv2D(flt, (1, 1), padding='same', activation='relu')(merge1)
upsample1 = Conv2DTranspose(**kwargs)(merge1)
fuse1 = concatenate([upsample1, conv11, co112, con113], axis=-1)
conv16 = conv_bn_relu(flt * 16, fuse1)
conv17 = conv_bn_relu(flt * 16, conv16)
upsample2 = Conv2DTranspose(**kwargs)(conv17)
fuse2 = concatenate([upsample2, conv8, co82, con83], axis=-1)
conv19 = conv_bn_relu(flt * 8, fuse2)
conv20 = conv_bn_relu(flt * 8, conv19)
upsample3 = Conv2DTranspose(**kwargs)(conv20)
fuse3 = concatenate([upsample3, conv5, co52, con53], axis=-1)
conv22 = conv_bn_relu(flt * 4, fuse3)
conv23 = conv_bn_relu(flt * 4, conv22)
upsample4 = Conv2DTranspose(**kwargs)(conv23)
fuse4 = concatenate([upsample4, conv2, co22, con23], axis=-1)
conv25 = conv_bn_relu(flt * 2, fuse4)
conv26 = conv_bn_relu(flt * 2, conv25)
model4 = path1(a)
model5 = path1(b)
model6 = path1(c)
merge2 = concatenate([conv26, model4, model5, model6], axis=-1)
merge2 =Conv2D(flt, (1, 1), padding='same', activation='relu')(merge2)
output = Conv2D(6, kernel_size=(1, 1), activation='softmax')(merge2)
model = Model(inputs=[a, b, c], outputs=output)
model.compile(optimizer=Adam(lr=0.0001), loss=[dice_coef_loss],
metrics=[class_mertics2, class_Edema, class_Scar])
#model.summary()
#plot_model(model, 'F:\\2020MICCAI_Cardiac_Segmentation\\Cardiac_Seg_MLM.png', show_shapes=True)
return model
def normolize(input):
epsilon = 1e-6
mean = np.mean(input)
std = np.std(input)
return (input-mean)/(std+epsilon)
def label_smoothing(inputs, epsilon=0.01):
return ((1-epsilon) * inputs) + (epsilon / 6)
def train():
train_C0 = np.load('F:\\train_data\\c0.npy')
train_DE = np.load('F:\\train_data\\lge.npy')
train_T2 = np.load('F:\\train_data\\t2.npy')
train_mask = np.load('F:\\train_data\\label.npy')
'''
train_C0 = np.load('F:\\2020MICCAI_Cardiac_Segmentation\\myops2020\\C0_train.npy')
train_DE = np.load('F:\\2020MICCAI_Cardiac_Segmentation\\myops2020\\DE_train.npy')
train_T2 = np.load('F:\\2020MICCAI_Cardiac_Segmentation\\myops2020\\T2_train.npy')
train_mask = np.load('F:\\2020MICCAI_Cardiac_Segmentation\\myops2020\\train_label.npy')
'''
train_C0 = normolize(train_C0)
train_DE = normolize(train_DE)
train_T2 = normolize(train_T2)
train_mask = label_smoothing(train_mask)
val1 = np.load('F:\\test_data\\c0.npy') # (11, 256, 256, 1)
val2 = np.load('F:\\test_data\\lge.npy') # (11, 256, 256, 1)
val3 = np.load('F:\\test_data\\t2.npy') # (11, 256, 256, 1)
val_label = np.load('F:\\test_data\\label.npy') # (11, 256, 256, 1)
val1 = normolize(val1)
val2 = normolize(val2)
val3 = normolize(val3)
val_label = label_smoothing(val_label)
earlystop = EarlyStopping(monitor='class_mertics2', patience=20, verbose=1, mode='max')
reduce_lr = ReduceLROnPlateau(monitor='class_mertics2', factor=0.1, patience=20, mode='max')
model = MLM()
#model.load_weights('F:\\2020MICCAI_Cardiac_Segmentation\\Cardiac_Seg_MLM.hdf5')
csv_logger = CSVLogger('Cardiac_Seg_MLM.csv')
model_checkpoint = ModelCheckpoint(filepath='Cardiac_Seg_MLM.hdf5', monitor='loss', verbose=1, save_best_only=True, mode= 'min')
model.fit([train_C0, train_DE, train_T2], train_mask, batch_size=4, validation_data=([val1, val2, val3], val_label), epochs=100, verbose=1, shuffle=True,
callbacks=[model_checkpoint, csv_logger, earlystop, reduce_lr])
if __name__ == '__main__':
train()