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BN_unet.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 23 10:01:27 2018
@author: shawn
"""
import tensorflow as tf
import tensorlayer as tl
import numpy as np
import BatchDatasetReader as BDR
import read_Data_list as RDL
import sys
import time
from sklearn.metrics import roc_auc_score
from sklearn.metrics import auc
#path variable
logs_dir = 'logs/'
data_dir = 'Data/'
#basic constant variable
IMG_SIZE = 512
num_of_classes = 2
print_freq = 10
#training constant variable
MAX_EPOCH = int(3000+1)
batch_size = 2
test_batchsize = 2
train_nbr = 44
test_nbr = 10
step_every_epoch = int(train_nbr/batch_size)
test_every_epoch = int(test_nbr/test_batchsize)
learning_rate = tf.Variable(1e-4, dtype=tf.float32)
#the parameters of aupr
range_threshold = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
#flags parameters
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string('mode', "train", "Mode train/ test/ visualize")
data_dir = "Data/"
class Unet:
def __init__(self, img_rows = IMG_SIZE, img_cols = IMG_SIZE):
self.img_rows = img_rows
self.img_cols = img_cols
def load_data_util(self):
image_options = {'resize': True, 'resize_size': IMG_SIZE} #resize all your images
train_records, valid_records = RDL.read_dataset(data_dir) #get read lists
train_dataset_reader = BDR.BatchDatset(train_records, image_options)
validation_dataset_reader = BDR.BatchDatset(valid_records, image_options)
return train_dataset_reader,validation_dataset_reader
def model(self, image, is_train=True, reuse=False):
with tf.variable_scope("model", reuse=reuse):
tl.layers.set_name_reuse(reuse)
W_init = tf.contrib.layers.xavier_initializer()
input_image = tl.layers.InputLayer(image, name='input_layer') #input image
conv2d_1 = tl.layers.Conv2d(input_image, 64, (3, 3), (1, 1),
padding='SAME', W_init=W_init, name='conv_1')
BN1 = tl.layers.BatchNormLayer(conv2d_1, act=tf.nn.relu, is_train=is_train, name='BN1')
conv2d_2 = tl.layers.Conv2d(BN1, 64, (3, 3), (1, 1),
padding='SAME', W_init=W_init, name='conv_2')
BN2 = tl.layers.BatchNormLayer(conv2d_2, act=tf.nn.relu, is_train=is_train, name='BN2')
pool_1 = tl.layers.MaxPool2d(BN2, (2, 2), (2,2),
padding='SAME', name='maxpool_1')
conv2d_3 = tl.layers.Conv2d(pool_1, 128, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_3')
BN3 = tl.layers.BatchNormLayer(conv2d_3, act=tf.nn.relu, is_train=is_train, name='BN3')
conv2d_4 = tl.layers.Conv2d(BN3, 128, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_4')
BN4 = tl.layers.BatchNormLayer(conv2d_4, act=tf.nn.relu, is_train=is_train, name='BN4')
pool_2 = tl.layers.MaxPool2d(BN4, (2,2), (2,2),
padding='SAME', name='maxpool_2')
conv2d_5 = tl.layers.Conv2d(pool_2, 256, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_5')
BN5 = tl.layers.BatchNormLayer(conv2d_5, act=tf.nn.relu, is_train=is_train, name='BN5')
conv2d_6 = tl.layers.Conv2d(BN5, 256, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_6')
BN6 = tl.layers.BatchNormLayer(conv2d_6, act=tf.nn.relu, is_train=is_train, name='BN6')
pool_3 = tl.layers.MaxPool2d(BN6, (2,2), (2,2),
padding='SAME', name='maxpool_3')
conv2d_7 = tl.layers.Conv2d(pool_3, 512, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_7')
BN7 = tl.layers.BatchNormLayer(conv2d_7, act=tf.nn.relu, is_train=is_train, name='BN7')
conv2d_8 = tl.layers.Conv2d(BN7, 512, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_8')
BN8 = tl.layers.BatchNormLayer(conv2d_8, act=tf.nn.relu, is_train=is_train, name='BN8')
#dropout_1 = tl.layers.DropoutLayer(conv2d_8, keep= 0.5, is_fix=True, is_train=is_train, name = 'drop_1')
pool_4 = tl.layers.MaxPool2d(BN8, (2,2), (2,2),
padding='SAME', name='maxpool_4')
conv2d_9 = tl.layers.Conv2d(pool_4, 1024, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_9')
BN9 = tl.layers.BatchNormLayer(conv2d_9, act=tf.nn.relu, is_train=is_train, name='BN9')
conv2d_10 = tl.layers.Conv2d(BN9, 1024, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_10')
BN10 = tl.layers.BatchNormLayer(conv2d_10, act=tf.nn.relu, is_train=is_train, name='BN10')
#dropout_2 = tl.layers.DropoutLayer(conv2d_10, keep= 0.5, is_fix=True, is_train=is_train, name = 'drop_2')
upsampling_1 = tl.layers.UpSampling2dLayer(BN10, (2,2), name='upsample2d_1')
conv2d_11 = tl.layers.Conv2d(upsampling_1, 512, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_11')
BN11 = tl.layers.BatchNormLayer(conv2d_11, act=tf.nn.relu, is_train=is_train, name='BN11')
concat_1 = tl.layers.ConcatLayer([BN8, BN11], 3, name ='concat_1')
conv2d_12 = tl.layers.Conv2d(concat_1, 512, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_12')
BN12 = tl.layers.BatchNormLayer(conv2d_12, act=tf.nn.relu, is_train=is_train, name='BN12')
conv2d_13 = tl.layers.Conv2d(BN12, 512, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_13')
BN13 = tl.layers.BatchNormLayer(conv2d_13, act=tf.nn.relu, is_train=is_train, name='BN13')
upsampling_2 = tl.layers.UpSampling2dLayer(BN13, (2,2), name='upsample2d_2')
conv2d_14 = tl.layers.Conv2d(upsampling_2, 256, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_14')
BN14 = tl.layers.BatchNormLayer(conv2d_14, act=tf.nn.relu, is_train=is_train, name='B14')
concat_2 = tl.layers.ConcatLayer([BN14,BN6], 3, name='concat_2')
conv2d_15 = tl.layers.Conv2d(concat_2, 256, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_15')
BN15 = tl.layers.BatchNormLayer(conv2d_15, act=tf.nn.relu, is_train=is_train, name='BN15')
conv2d_16 = tl.layers.Conv2d(BN15, 256, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_16')
BN16 = tl.layers.BatchNormLayer(conv2d_16, act=tf.nn.relu, is_train=is_train, name='BN16')
upsampling_3 = tl.layers.UpSampling2dLayer(BN16, (2,2), name='upsample2d_3')
conv2d_17 = tl.layers.Conv2d(upsampling_3, 128, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_17')
BN17 = tl.layers.BatchNormLayer(conv2d_17, act=tf.nn.relu, is_train=is_train, name='BN17')
concat_3 = tl.layers.ConcatLayer([BN17,BN4], 3, name='concat_3')
conv2d_18 = tl.layers.Conv2d(concat_3, 128, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_18')
BN18 = tl.layers.BatchNormLayer(conv2d_18, act=tf.nn.relu, is_train=is_train, name='BN18')
conv2d_19 = tl.layers.Conv2d(BN18, 128, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_19')
BN19 = tl.layers.BatchNormLayer(conv2d_19, act=tf.nn.relu, is_train=is_train, name='BN19')
upsampling_4 = tl.layers.UpSampling2dLayer(BN19, (2,2), name='upsample2d_4')
conv2d_20 = tl.layers.Conv2d(upsampling_4, 64, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_20')
BN20 = tl.layers.BatchNormLayer(conv2d_20, act=tf.nn.relu, is_train=is_train, name='BN20')
concat_4 = tl.layers.ConcatLayer([BN20,BN2], 3, name='concat_4')
conv2d_21 = tl.layers.Conv2d(concat_4, 64, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_21')
BN21 = tl.layers.BatchNormLayer(conv2d_21, act=tf.nn.relu, is_train=is_train, name='BN21')
conv2d_22 = tl.layers.Conv2d(BN21, 32, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_22')
BN22 = tl.layers.BatchNormLayer(conv2d_22, act=tf.nn.relu, is_train=is_train, name='BN22')
conv2d_23 = tl.layers.Conv2d(BN22, num_of_classes, (3,3), (1,1),
padding='SAME', W_init=W_init, name='conv_23')
#maybe conv2d_23 should not be activation!
y = conv2d_23.outputs #transfer tl object to logits tensor
pred = tf.argmax(y, dimension=3, name="prediction")
return pred,y,conv2d_23
def loss(self, logits, annotation):
loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=tf.squeeze(annotation, squeeze_dims=[3]),
name="entropy")))
L2 = 0
for p in tl.layers.get_variables_with_name('relu/W', True, True):
L2 += tf.contrib.layers.l2_regularizer(0.004)(p)
cost = loss + L2
return loss
def train(self, loss):
#If use tf.nn.sparse_softmax_cross_entropy_with_logits ,
#maybe loss will be NAN,because without clip
#annotation = tf.cast(annotation,dtype = tf.float32)
#prob = tf.nn.softmax(logits)
#loss = -tf.reduce_mean(annotation*tf.log(tf.clip_by_value(prob,1e-11,1.0)))
optimizer = tf.train.AdamOptimizer(learning_rate)
var_list = tf.trainable_variables()
grads = optimizer.compute_gradients(loss, var_list=var_list)
train_op = optimizer.apply_gradients(grads)
return train_op
#AUPR score
def computeConfMatElements(thresholded_proba_map, ground_truth):
P = np.count_nonzero(ground_truth)
TP = np.count_nonzero(thresholded_proba_map*ground_truth)
FP = np.count_nonzero(thresholded_proba_map - (thresholded_proba_map*ground_truth))
return P,TP,FP
def computeAUPR(proba_map, ground_truth, threshold_list):
proba_map = proba_map.astype(np.float32)
precision_list_treshold = []
recall_list_treshold = []
#loop over thresholds
for threshold in threshold_list:
precision_list_proba_map = []
recall_list_proba_map = []
#loop over proba map
for i in range(len(proba_map)): #batch_size
#threshold the proba map
thresholded_proba_map = np.zeros(np.shape(proba_map[i]))
thresholded_proba_map[proba_map[i] >= threshold] = 1
#print(np.shape(thresholded_proba_map)) #(400,640)
#compute P, TP, and FP for this threshold and this proba map
P,TP,FP = computeConfMatElements(thresholded_proba_map, ground_truth[i])
#check that ground truth contains at least one positive
if (P > 0 and (TP+FP) > 0) :
precision_list_proba_map.append(TP*1./(TP+FP))
recall_list_proba_map.append(TP*1./P)
else:
precision_list_proba_map.append(0)
recall_list_proba_map.append(0)
#average sensitivity and FP over the proba map, for a given threshold
precision_list_treshold.append(np.mean(precision_list_proba_map))
recall_list_treshold.append(np.mean(recall_list_proba_map))
return auc(recall_list_treshold, precision_list_treshold)
def main(argv=None):
myUnet = Unet()
image = tf.placeholder(tf.int32,[None,IMG_SIZE,IMG_SIZE,1], name='image') #input gray images
annotation = tf.placeholder(tf.int32, shape=[None, IMG_SIZE, IMG_SIZE, 1], name="annotation")
image = tf.cast(image,tf.float32)
annotation = tf.cast(annotation,tf.int32)
# define inferences
train_pred, train_logits, train_tlnetwork = myUnet.model(image, is_train=True, reuse=False)
train_positive_prob = tf.nn.softmax(train_logits)[:, :, :, 1]
train_loss_op = myUnet.loss(train_logits, annotation)
train_op = myUnet.train(train_loss_op)
test_pred, test_logits, test_tlnetwork = myUnet.model(image, is_train=False, reuse=True)
test_positive_prob = tf.nn.softmax(test_logits)[:, :, :, 1]
test_loss_op= myUnet.loss(test_logits, annotation)
lr_assign_op = tf.assign(learning_rate, learning_rate / 10) #learning_rate decay
#only visualize the test images
#first lighten the annotation images
visual_annotation = tf.where(tf.equal(annotation,1), annotation+254, annotation)
visual_pred = tf.expand_dims(tf.where(tf.equal(test_pred,1), test_pred+254, test_pred), dim=3)
tf.summary.image("input_image", image, max_outputs=2)
tf.summary.image("ground_truth", tf.cast(visual_annotation, tf.uint8), max_outputs=2)
tf.summary.image("pred_annotation", tf.cast(visual_pred, tf.uint8), max_outputs=2)
print("Setting up summary op...")
test_summary_op = tf.summary.merge_all()
if FLAGS.mode == 'train':
train_dataset_reader,validation_dataset_reader = myUnet.load_data_util()
sess = tf.Session()
print("Setting up Saver...")
saver = tf.train.Saver(max_to_keep=2)
summary_writer = tf.summary.FileWriter(logs_dir, sess.graph)
tl.layers.initialize_global_variables(sess)
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(logs_dir)#if model has been trained,restore it
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored...")
for epo in range(MAX_EPOCH):
start_time = time.time()
train_loss, test_loss, train_aupr, test_aupr, train_auc, test_auc= 0, 0, 0, 0, 0, 0
for s in range(step_every_epoch):
train_images, train_annotations = train_dataset_reader.next_batch(batch_size)
feed_dict = {image: train_images, annotation: train_annotations}
tra_positive_prob, train_err, _ = sess.run([train_positive_prob, train_loss_op,train_op], feed_dict=feed_dict)
#compute auc score
temp_train_annotations = np.reshape(train_annotations,-1)
temp_tra_positive_prob = np.reshape(tra_positive_prob,-1)
train_sauc = roc_auc_score(temp_train_annotations, temp_tra_positive_prob)
#compute aupr
train_saupr = computeAUPR(tra_positive_prob ,np.squeeze(train_annotations, axis=3), range_threshold)
train_loss += train_err
train_auc += train_sauc
train_aupr += train_saupr
if epo + 1 == 1 or (epo + 1) % print_freq == 0:
train_loss = train_loss/step_every_epoch
train_auc = train_auc/step_every_epoch
train_aupr = train_aupr/step_every_epoch
#visualize the training loss
print("%d epoches %d took %fs" % (print_freq, epo, time.time() - start_time))
print(" train loss: %f" % train_loss)
print(" train auc: %f" % train_auc)
print(" train aupr: %f" % train_aupr)
train_summary = tf.Summary(value=[
tf.Summary.Value(tag="train_loss", simple_value=train_loss),
tf.Summary.Value(tag="train_auc", simple_value=train_auc),
tf.Summary.Value(tag="train_aupr", simple_value=train_aupr)
])
summary_writer.add_summary(train_summary, epo)
for test_s in range(test_every_epoch):
#get validation data
valid_images, valid_annotations = validation_dataset_reader.next_batch(test_batchsize)
#visualize the validation loss
feed_dict= {image:valid_images,annotation:valid_annotations}
valid_positive_prob, validation_err = sess.run([test_positive_prob, test_loss_op], feed_dict= feed_dict)
#compute auc score
temp_valid_annotations = np.reshape(valid_annotations,-1)
temp_valid_positive_prob = np.reshape(valid_positive_prob,-1)
test_sauc = roc_auc_score(temp_valid_annotations, temp_valid_positive_prob)
#compute test aupr
test_saupr = computeAUPR(valid_positive_prob ,np.squeeze(valid_annotations, axis=3), range_threshold)
test_loss += validation_err
test_auc += test_sauc
test_aupr += test_saupr
test_loss = test_loss/test_every_epoch
test_auc = test_auc/test_every_epoch
test_aupr = test_aupr/test_every_epoch
test_summary = tf.Summary(value=[
tf.Summary.Value(tag="test_loss", simple_value=test_loss),
tf.Summary.Value(tag="test_auc", simple_value=test_auc),
tf.Summary.Value(tag="test_aupr", simple_value=test_aupr)
])
summary_writer.add_summary(test_summary, epo)
#visualize the test result(only visualize the last batchsize of this epoch)
feed_dict= {image:valid_images,annotation:valid_annotations}
summary_str = sess.run(test_summary_op, feed_dict = feed_dict)
summary_writer.add_summary(summary_str, epo)
#summary_writer.add_summary(summary_str, epo)
#tensorboard flush
summary_writer.flush()
sys.stdout.flush()
#if epo == 1000 or epo == 2000:
# sess.run(lr_assign_op)
if epo % 3000 == 0:
saver.save(sess, logs_dir + "model.ckpt", epo)
print('the %d epoch , the model has been saved successfully' %epo)
sys.stdout.flush()
if __name__ == '__main__':
tf.app.run()