###################################################
#
Script to:
- Load the images and extract the patches
- Define the neural network
- define the training
#
##################################################
import numpy as np
import configparser
from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, UpSampling2D, Reshape, core, Dropout
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as K
from keras.utils.vis_utils import plot_model as plot
from keras.optimizers import SGD
import sys
sys.path.insert(0, 'C:\Users\Administrator\Desktop\袁炀\最新下载的python3的完整版\Retina-Unet-master\lib\')
from help_functions import *
#function to obtain data for training/testing (validation)
from extract_patches import get_data_training
print('0step')
#Define the neural network
def get_unet(n_ch,patch_height,patch_width):
inputs = Input(shape=(n_ch,patch_height,patch_width))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same',data_format='channels_first')(inputs)
conv1 = Dropout(0.2)(conv1)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same',data_format='channels_first')(conv1)
pool1 = MaxPooling2D((2, 2))(conv1)
#
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same',data_format='channels_first')(pool1)
conv2 = Dropout(0.2)(conv2)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same',data_format='channels_first')(conv2)
pool2 = MaxPooling2D((2, 2))(conv2)
#
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same',data_format='channels_first')(pool2)
conv3 = Dropout(0.2)(conv3)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same',data_format='channels_first')(conv3)
up1 = UpSampling2D(size=(2, 2))(conv3)
up1 = concatenate([conv2,up1],axis=1)
conv4 = Conv2D(64, (3, 3), activation='relu', padding='same',data_format='channels_first')(up1)
conv4 = Dropout(0.2)(conv4)
conv4 = Conv2D(64, (3, 3), activation='relu', padding='same',data_format='channels_first')(conv4)
#
up2 = UpSampling2D(size=(2, 2))(conv4)
up2 = concatenate([conv1,up2], axis=1)
conv5 = Conv2D(32, (3, 3), activation='relu', padding='same',data_format='channels_first')(up2)
conv5 = Dropout(0.2)(conv5)
conv5 = Conv2D(32, (3, 3), activation='relu', padding='same',data_format='channels_first')(conv5)
#
conv6 = Conv2D(2, (1, 1), activation='relu',padding='same',data_format='channels_first')(conv5)
conv6 = core.Reshape((2,patch_height*patch_width))(conv6)
conv6 = core.Permute((2,1))(conv6)
############
conv7 = core.Activation('softmax')(conv6)
model = Model(inputs=inputs, outputs=conv7)
# sgd = SGD(lr=0.01, decay=1e-6, momentum=0.3, nesterov=False)
model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics = ['accuracy'])
####编译模型https://www.cnblogs.com/LittleHann/p/6442161.html
return model
print('1step')
#Define the neural network gnet
#you need change function call "get_unet" to "get_gnet" in line 166 before use this network
def get_gnet(n_ch,patch_height,patch_width):
inputs = Input((n_ch, patch_height, patch_width))
conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(inputs)
conv1 = Dropout(0.2)(conv1)
conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv1)
up1 = UpSampling2D(size=(2, 2))(conv1)
#
conv2 = Convolution2D(16, 3, 3, activation='relu', border_mode='same')(up1)
conv2 = Dropout(0.2)(conv2)
conv2 = Convolution2D(16, 3, 3, activation='relu', border_mode='same')(conv2)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv2)
#
conv3 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(pool1)
conv3 = Dropout(0.2)(conv3)
conv3 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv3)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv3)
#
conv4 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(pool2)
conv4 = Dropout(0.2)(conv4)
conv4 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv4)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv4)
#
conv5 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(pool3)
conv5 = Dropout(0.2)(conv5)
conv5 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv5)
#
up2 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1)
conv6 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(up2)
conv6 = Dropout(0.2)(conv6)
conv6 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv6)
#
up3 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=1)
conv7 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(up3)
conv7 = Dropout(0.2)(conv7)
conv7 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv7)
#
up4 = merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=1)
conv8 = Convolution2D(16, 3, 3, activation='relu', border_mode='same')(up4)
conv8 = Dropout(0.2)(conv8)
conv8 = Convolution2D(16, 3, 3, activation='relu', border_mode='same')(conv8)
#
pool4 = MaxPooling2D(pool_size=(2, 2))(conv8)
conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(pool4)
conv9 = Dropout(0.2)(conv9)
conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv9)
#
conv10 = Convolution2D(2, 1, 1, activation='relu', border_mode='same')(conv9)
conv10 = core.Reshape((2,patch_height*patch_width))(conv10)
conv10 = core.Permute((2,1))(conv10)
############
conv10 = core.Activation('softmax')(conv10)
model = Model(input=inputs, output=conv10)
# sgd = SGD(lr=0.01, decay=1e-6, momentum=0.3, nesterov=False)
model.compile(optimizer='sgd', loss='categorical_crossentropy',metrics=['accuracy'])
return model
print('2step')
#========= Load settings from Config file
config = configparser.RawConfigParser()
config.read('configuration.txt')
#patch to the datasets
path_data = config.get('data paths', 'path_local')
#Experiment name
name_experiment = config.get('experiment name', 'name')
#training settings
N_epochs = int(config.get('training settings', 'N_epochs'))
batch_size = int(config.get('training settings', 'batch_size'))
#============ Load the data and divided in patches
patches_imgs_train, patches_masks_train = get_data_training(
DRIVE_train_imgs_original = path_data + config.get('data paths', 'train_imgs_original'),
DRIVE_train_groudTruth = path_data + config.get('data paths', 'train_groundTruth'), #masks
patch_height = int(config.get('data attributes', 'patch_height')),
patch_width = int(config.get('data attributes', 'patch_width')),
N_subimgs = int(config.get('training settings', 'N_subimgs')),
inside_FOV = config.getboolean('training settings', 'inside_FOV') #select the patches only inside the FOV (default == True)
)
####具体函数在extract_patch中,获得批量的图和批量的提取的图
print('3step')
#========= Save a sample of what you're feeding to the neural network ==========
N_sample = min(patches_imgs_train.shape[0],40)###patches_imgs_train.shape为(190000, 1, 48, 48)，即patches_imgs_train.shape[0]为19000
visualize(group_images(patches_imgs_train[0:N_sample,:,:,:],5),'./'+name_experiment+'/'+"sample_input_imgs").show()#对数据集划分，进行分组显示
visualize(group_images(patches_masks_train[0:N_sample,:,:,:],5),'./'+name_experiment+'/'+"sample_input_masks").show()
print('4step')
#=========== Construct and save the model arcitecture =====
n_ch = patches_imgs_train.shape[1]
patch_height = patches_imgs_train.shape[2]
patch_width = patches_imgs_train.shape[3]
model = get_unet(n_ch, patch_height, patch_width) #the U-net model
print("Check: final output of the network:")
print(model.output_shape)
import os
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'
plot(model, to_file='./'+name_experiment+'/'+name_experiment + '_model.png') #check how the model looks like
json_string = model.to_json()
open('./'+name_experiment+'/'+name_experiment +'_architecture.json', 'w').write(json_string)
###调用网络 及 保存网络模型
print('5step')
#============ Training ==================================
checkpointer = ModelCheckpoint(filepath='./'+name_experiment+'/'+name_experiment +'_best_weights.h5', verbose=1, monitor='val_loss', mode='auto', save_best_only=True) #save at each epoch if the validation decreased
print('6step')
def step_decay(epoch):
lrate = 0.01 #the initial learning rate (by default in keras)
if epoch==100:
return 0.005
else:
return lrate
lrate_drop = LearningRateScheduler(step_decay)
##动态调整学习率并实时保存each epoch的checkpoint数据
print('7step')
patches_masks_train=np.load("patch_woxiede.npy")
###patches_masks_train = masks_Unet(patches_masks_train) reduce memory consumption？？？？？？？？？？？？？减少内存消耗patches_masks_train的shape是(190000, 1, 48, 48)
print('8step')
model.fit(patches_imgs_train, patches_masks_train, epochs=N_epochs, batch_size=batch_size, verbose=1, shuffle=True, validation_split=0.1, callbacks=[checkpointer])
print('9step')
#========== Save and test the last model ===================
model.save_weights('./'+name_experiment+'/'+name_experiment +'_last_weights.h5', overwrite=True)
#test the model
