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unet_v2.py
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unet_v2.py
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from __future__ import print_function
import os
from skimage.transform import resize
from skimage.io import imsave
import numpy as np
from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.optimizers import Adam, SGD
from keras.callbacks import ModelCheckpoint
from keras import backend as K
from keras import initializers
from keras.layers.core import Dropout
from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from itertools import izip
# define the input size
img_rows = 512
img_cols = 512
channels = 1
# parameter for loss function
smooth = 1.
# input data
train_data_path = "E:/肝脏数据/nii/img/train-img/"
validation_data_path = 'E:/肝脏数据/nii/img/valid-img/'
train_mask_data_path = 'E:/肝脏数据/nii/label/train-label/'
validation_mask_data_path = 'E:/肝脏数据/nii/label/valid-label/'
# define u-net architecture
def UNET():
inputs = Input((img_rows, img_cols, channels))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(conv4)
conv4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(conv5)
conv5 = Dropout(0.5)(conv5)
up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same',kernel_initializer='he_normal')(conv5), conv4], axis=3)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(up6)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(conv6)
up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same',kernel_initializer='he_normal')(conv6), conv3], axis=3)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(up7)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(conv7)
up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same',kernel_initializer='he_normal')(conv7), conv2], axis=3)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(up8)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(conv8)
up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same',kernel_initializer='he_normal')(conv8), conv1], axis=3)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(up9)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same',kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)
model = Model(inputs=[inputs], outputs=[conv10])
# model.compile(optimizer=Adam(lr=1e-5), loss='binary_crossentropy', metrics=[dice_coef])
model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])
# sgd = SGD(lr=0.1, momentum=0.99, decay=1, nesterov=False)
# model.compile(optimizer=sgd, loss=dice_coef_loss, metrics=[dice_coef])
return model
# preprocess(imgs) will not be used in u-net V2
def preprocess(imgs):
imgs_p = np.ndarray((imgs.shape[0], img_rows, img_cols), dtype=np.uint8)
print("shape of imgs_p is {}".format(imgs_p.shape))
for i in range(imgs.shape[0]):
imgs_p[i] = imgs[i]
imgs_p = imgs_p[..., np.newaxis]
print("shape of imgs_p is {}".format(imgs_p.shape))
return imgs_p
# loss function
def dice_coef(y_true, y_pred):
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_coef_loss(y_true, y_pred):
return -dice_coef(y_true, y_pred)
# trian u-net and validate it
def train_and_predict():
# initial train image data generater
data_gen_args = dict(rotation_range=30.,
width_shift_range=0.1,
height_shift_range=0.1,
fill_mode = 'constant',
cval=0,
rescale=1./255)
image_datagen = ImageDataGenerator(**data_gen_args)
mask_datagen = ImageDataGenerator(**data_gen_args)
# Provide the same seed and keyword arguments to the fit and flow methods
seed = 1
image_generator = image_datagen.flow_from_directory(
train_data_path,
target_size=(512,512),
color_mode='grayscale',
class_mode=None,
seed=seed,
batch_size=16)
mask_generator = mask_datagen.flow_from_directory(
train_mask_data_path,
target_size=(512,512),
color_mode='grayscale',
class_mode=None,
seed=seed,
batch_size=16)
# get train generator
train_generator = izip(image_generator, mask_generator)
# initial validation image data generater
validation_gen_args = dict(rescale=1./255)
validation_image_datagen = ImageDataGenerator(**validation_gen_args)
validation_mask_datagen = ImageDataGenerator(**validation_gen_args)
seed2 = 2
validation_img_generator = validation_image_datagen.flow_from_directory(
validation_data_path,
target_size=(512,512),
color_mode='grayscale',
class_mode=None,
seed=seed2,
batch_size=16)
validation_mask_generator = validation_mask_datagen.flow_from_directory(
validation_mask_data_path,
target_size=(512,512),
color_mode='grayscale',
class_mode=None,
seed=seed2,
batch_size=16)
validation_generator = izip(validation_img_generator, validation_mask_generator)
# instantiate a U-net
model = UNET()
model_checkpoint = ModelCheckpoint('40000_weights.h5', monitor='val_loss', save_best_only=True)
model.fit_generator(train_generator, steps_per_epoch=1150, epochs=3000, verbose=1,
validation_data=validation_generator,
validation_steps=14,
callbacks=[model_checkpoint])
if __name__ == '__main__':
train_and_predict()