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import keras
from keras import backend as K
from keras.layers import Input
from keras.layers.convolutional import Conv2D,Conv2DTranspose
from keras.layers.pooling import MaxPooling2D
from keras.layers.merge import concatenate
from keras.losses import binary_crossentropy
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import Callback,ModelCheckpoint,EarlyStopping,ReduceLROnPlateau
from utils import gen,DataGenerator
import numpy as np
import json
class Config(object):
batch_size = 32
backbone = 'resnet34'
encoding_weights = 'imagenet'
activation = 'sigmoid'
epochs = 100
learning_rate = 3e-4
height = 320
width = 480
channels = 3
es_patience = 5
rlrop_patience = 3
decay_drop = 0.5
n_classes = 4
def dice_coef(y_true,y_pred,smooth=1):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2.* intersection + smooth) / (K.sum(y_true_f)+K.sum(y_pred_f)+smooth)
def dice_loss(y_true,y_pred):
smooth = 1.
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = y_true_f * y_pred_f
score = (2. * K.sum(intersection)+smooth) / (K.sum(y_true_f)+K.sum(y_pred_f)+smooth)
return 1. - score
def bce_dice_loss(y_true,y_pred):
return binary_crossentropy(y_true,y_pred) + dice_loss(y_true,y_pred)
def unet(input_shape):
inputs = Input(shape=input_shape)
'elu---> Exponential linear unit'
c1 = Conv2D(8,(3,3),activation='elu',padding='same')(inputs)
c1 = Conv2D(8,(3,3),activation='elu',padding='same')(c1)
p1 = MaxPooling2D((2,2),padding='same')(c1)
c2 = Conv2D(16,(3,3),activation='elu',padding='same')(p1)
c2 = Conv2D(16,(3,3),activation='elu',padding='same')(c2)
p2 = MaxPooling2D((2,2),padding='same')(c2)
c3 = Conv2D(32,(3,3),activation='elu',padding='same')(p2)
c3 = Conv2D(32,(3,3),activation='elu',padding='same')(c3)
p3 = MaxPooling2D((2,2),padding='same')(c3)
c4 = Conv2D(64,(3,3),activation='elu',padding='same')(p3)
c4 = Conv2D(64,(3,3),activation='elu',padding='same')(c4)
p4 = MaxPooling2D((2,2),padding='same')(c4)
c5 = Conv2D(64,(3,3),activation='elu',padding='same')(p4)
c5 = Conv2D(64,(3,3),activation='elu',padding='same')(c5)
p5 = MaxPooling2D((2,2),padding='same')(c5)
c55 = Conv2D(128,(3,3),activation='elu',padding='same')(p5)
c55 = Conv2D(128,(3,3),activation='elu',padding='same')(c55)
u6 = Conv2DTranspose(64,(2,2),strides=(2,2),padding='same')(c55)
u6 = concatenate([u6,c5])
c6 = Conv2D(64,(3,3),activation='elu',padding='same')(u6)
c6 = Conv2D(64,(3,3),activation='elu',padding='same')(c6)
u71 = Conv2DTranspose(32,(2,2),strides=(2,2),padding='same')(c6)
u71 = concatenate([u71,c4])
c71 = Conv2D(32,(3,3),activation='elu',padding='same')(u71)
c61 = Conv2D(32,(3,3),activation='elu',padding='same')(c71)
u7 = Conv2DTranspose(32,(2,2),strides=(2,2),padding='same')(c61)
u7 = concatenate([u7,c3])
c7 = Conv2D(32,(3,3),activation='elu',padding='same')(u7)
c7 = Conv2D(32,(3,3),activation='elu',padding='same')(c7)
u8 = Conv2DTranspose(16,(2,2),strides=(2,2),padding='same')(c7)
u8 = concatenate([u8,c2])
c8 = Conv2D(16,(3,3),activation='elu',padding='same')(u8)
c8 = Conv2D(16,(3,3),activation='elu',padding='same')(c8)
u9 = Conv2DTranspose(8,(2,2),strides=(2,2),padding='same')(c8)
u9 = concatenate([u9,c1],axis=3)
c9 = Conv2D(8,(3,3),activation='elu',padding='same')(u9)
c9 = Conv2D(8,(3,3),activation='elu',padding='same')(c9)
outputs = Conv2D(4,(1,1),activation='sigmoid')(c9)
model = Model(inputs=[inputs],outputs=[outputs])
return model
def train(inputs,data):
model = unet(inputs)
model.summary()
train_idx,mask_count_df,train_df,val_idx = data
config = Config()
train_generator = DataGenerator(train_idx,
df=mask_count_df,
target_df=train_df,
batch_size=config.batch_size,
reshape=(config.height,config.width),
augment=True,
graystyle=False,
shuffle = True,
n_channels=config.channels,
n_classes=config.n_classes)
train_eval_generator = DataGenerator(train_idx,
df=mask_count_df,
target_df=train_df,
batch_size=config.batch_size,
reshape=(config.height,config.width),
augment=False,
graystyle=False,
shuffle = False,
n_channels=config.channels,
n_classes=config.n_classes)
val_generator = DataGenerator(val_idx,
df=mask_count_df,
target_df=train_df,
batch_size=config.batch_size,
reshape=(config.height,config.width),
augment=False,
graystyle=False,
shuffle = False,
n_channels=config.channels,
n_classes=config.n_classes)
earlystopping = EarlyStopping(monitor='loss',patience=config.es_patience)
reduce_lr = ReduceLROnPlateau(monitor='loss',patience=config.rlrop_patience,factor=config.decay_drop,min_lr=1e-6)
checkpoint = ModelCheckpoint(filepath='weights-{epoch:03d}-{loss:.2f}.h5',monitor='loss',save_best_only=False,save_weights_only=True)
metric_list = [dice_coef]
callback_list = [earlystopping,reduce_lr,checkpoint]
optimizer = Adam(lr=config.learning_rate)
model.compile(optimizer=optimizer,loss=bce_dice_loss,metrics=metric_list)
checkpoint.set_model(model)
model.fit_generator(train_generator,validation_data=val_generator,callbacks=callback_list,epochs=100,initial_epoch=0)
if __name__ == '__main__':
csv = 'data/train.csv'
train_idx,mask_count_df,train_df,val_idx = gen(csv,False)
data = (train_idx,mask_count_df,train_df,val_idx)
model = unet((320,480,3))
train((320,480,3),data)