def test(self, dicom_dir):
# X = tf.placeholder(shape=[batch_size, input_shape[0], input_shape[1], input_shape[2]], dtype=tf.float32)
test_input_shape = input_shape
test_batch_size = batch_size * 2
threshold = tf.placeholder(tf.float32)
training = tf.placeholder(tf.bool)
X = tf.placeholder(shape=[
test_batch_size, test_input_shape[0], test_input_shape[1],
test_input_shape[2]
],
dtype=tf.float32)
with tf.variable_scope('ae'):
Y_pred, Y_pred_modi, Y_pred_nosig = self.ae_u(
X, training, test_batch_size, threshold)
print tools.Ops.variable_count()
sum_merged = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=1)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.visible_device_list = GPU0
with tf.Session(config=config) as sess:
# if os.path.exists(self.train_models_dir):
# saver.restore(sess, self.train_models_dir + 'model.cptk')
sum_writer_train = tf.summary.FileWriter(self.train_sum_dir,
sess.graph)
sum_write_test = tf.summary.FileWriter(self.test_sum_dir)
if os.path.exists(self.train_models_dir) and os.path.isfile(
self.train_models_dir + 'model.cptk.data-00000-of-00001'):
print "restoring saved model"
saver.restore(sess, self.train_models_dir + 'model.cptk')
else:
sess.run(tf.global_variables_initializer())
test_data = tools.Test_data(dicom_dir, input_shape)
test_data.organize_blocks()
block_numbers = test_data.blocks.keys()
for i in range(0, len(block_numbers), test_batch_size):
batch_numbers = []
if i + test_batch_size < len(block_numbers):
temp_input = np.zeros([
test_batch_size, input_shape[0], input_shape[1],
input_shape[2]
])
for j in range(test_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0], 0:block_shape[1],
0:block_shape[2]] += block_array
Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
[Y_pred, Y_pred_modi, Y_pred_nosig],
feed_dict={
X: temp_input,
training: False,
threshold: upper_threshold
})
for j in range(test_batch_size):
test_data.upload_result(batch_numbers[j],
Y_temp_modi[j, :, :, :])
else:
temp_batch_size = len(block_numbers) - i
temp_input = np.zeros([
temp_batch_size, input_shape[0], input_shape[1],
input_shape[2]
])
for j in range(temp_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0], 0:block_shape[1],
0:block_shape[2]] += block_array
X_temp = tf.placeholder(shape=[
temp_batch_size, input_shape[0], input_shape[1],
input_shape[2]
],
dtype=tf.float32)
with tf.variable_scope('ae', reuse=True):
Y_pred_temp, Y_pred_modi_temp, Y_pred_nosig_temp = self.ae_u(
X_temp, training, temp_batch_size, threshold)
Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
[Y_pred_temp, Y_pred_modi_temp, Y_pred_nosig_temp],
feed_dict={
X_temp: temp_input,
training: False,
threshold: upper_threshold
})
for j in range(temp_batch_size):
test_data.upload_result(batch_numbers[j],
Y_temp_modi[j, :, :, :])
test_result_array = test_data.get_result()
print "result shape: ", np.shape(test_result_array)
r_s = np.shape(test_result_array) # result shape
e_t = 10 # edge thickness
to_be_transformed = np.zeros(r_s, np.float32)
to_be_transformed[e_t:r_s[0] - e_t, e_t:r_s[1] - e_t, 0:r_s[2] -
e_t] += test_result_array[e_t:r_s[0] - e_t,
e_t:r_s[1] - e_t,
0:r_s[2] - e_t]
print np.max(to_be_transformed)
print np.min(to_be_transformed)
final_img = ST.GetImageFromArray(
np.transpose(to_be_transformed, [2, 1, 0]))
final_img.SetSpacing(test_data.space)
print "writing final testing result"
print './test_result/test_result_final.vtk'
ST.WriteImage(final_img, './test_result/test_result_final.vtk')
return final_img
def full_testing(self, sess, X, w, threshold, test_merge_op,
sum_write_test, training, weight_for, total_acc, Y_pred,
Y_pred_modi, Y_pred_nosig, epoch):
print '********************** FULL TESTING ********************************'
# X = tf.placeholder(shape=[batch_size, input_shape[0], input_shape[1], input_shape[2]], dtype=tf.float32)
# w = tf.placeholder(tf.float32)
# threshold = tf.placeholder(tf.float32)
time_begin = time.time()
origin_data = read_dicoms(test_dir + "original1")
mask_dir = test_dir + "airway"
test_batch_size = batch_size
# test_data = tools.Test_data(dicom_dir,input_shape)
test_data = tools.Test_data(origin_data, input_shape, 'vtk_data')
test_data.organize_blocks()
test_mask = read_dicoms(mask_dir)
array_mask = ST.GetArrayFromImage(test_mask)
array_mask = np.transpose(array_mask, (2, 1, 0))
print "mask shape: ", np.shape(array_mask)
block_numbers = test_data.blocks.keys()
for i in range(0, len(block_numbers), test_batch_size):
batch_numbers = []
if i + test_batch_size < len(block_numbers):
temp_input = np.zeros([
test_batch_size, input_shape[0], input_shape[1],
input_shape[2]
])
for j in range(test_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0], 0:block_shape[1],
0:block_shape[2]] += block_array
Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
[Y_pred, Y_pred_modi, Y_pred_nosig],
feed_dict={
X: temp_input,
training: False,
w: weight_for,
threshold: upper_threshold + test_extra_threshold
})
for j in range(test_batch_size):
test_data.upload_result(batch_numbers[j],
Y_temp_modi[j, :, :, :])
else:
temp_batch_size = len(block_numbers) - i
temp_input = np.zeros([
temp_batch_size, input_shape[0], input_shape[1],
input_shape[2]
])
for j in range(temp_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0], 0:block_shape[1],
0:block_shape[2]] += block_array
X_temp = tf.placeholder(shape=[
temp_batch_size, input_shape[0], input_shape[1],
input_shape[2]
],
dtype=tf.float32)
with tf.variable_scope('generator', reuse=True):
Y_pred_temp, Y_pred_modi_temp, Y_pred_nosig_temp = self.ae_u(
X_temp, training, temp_batch_size, threshold)
Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
[Y_pred_temp, Y_pred_modi_temp, Y_pred_nosig_temp],
feed_dict={
X_temp: temp_input,
training: False,
w: weight_for,
threshold: upper_threshold + test_extra_threshold
})
for j in range(temp_batch_size):
test_data.upload_result(batch_numbers[j],
Y_temp_modi[j, :, :, :])
test_result_array = test_data.get_result()
print "result shape: ", np.shape(test_result_array)
to_be_transformed = self.post_process(test_result_array)
if epoch == total_test_epoch:
mask_img = ST.GetImageFromArray(np.transpose(
array_mask, [2, 1, 0]))
mask_img.SetSpacing(test_data.space)
ST.WriteImage(mask_img, self.test_results_dir + 'test_mask.vtk')
test_IOU = 2 * np.sum(to_be_transformed * array_mask) / (
np.sum(to_be_transformed) + np.sum(array_mask))
test_summary = sess.run(test_merge_op, feed_dict={total_acc: test_IOU})
sum_write_test.add_summary(test_summary, global_step=epoch)
print "IOU accuracy: ", test_IOU
time_end = time.time()
print '******************** time of full testing: ' + str(
time_end - time_begin) + 's ********************'
def train(self, configure):
data = tools.Data(configure, epoch_walked)
best_acc = 0
# X = tf.placeholder(shape=[batch_size, input_shape[0], input_shape[1], input_shape[2]], dtype=tf.float32)
X = tf.placeholder(
shape=[batch_size, input_shape[0], input_shape[1], input_shape[2]],
dtype=tf.float32)
# Y = tf.placeholder(shape=[batch_size, output_shape[0], output_shape[1], output_shape[2]], dtype=tf.float32)
Y = tf.placeholder(shape=[
batch_size, output_shape[0], output_shape[1], output_shape[2]
],
dtype=tf.float32)
print X.get_shape()
lr = tf.placeholder(tf.float32)
training = tf.placeholder(tf.bool)
threshold = tf.placeholder(tf.float32)
with tf.variable_scope('ae'):
Y_pred, Y_pred_modi, Y_pred_nosig = self.ae_u(
X, training, batch_size, threshold)
with tf.variable_scope('dis'):
XY_real_pair = self.dis(X, Y, training)
with tf.variable_scope('dis', reuse=True):
XY_fake_pair = self.dis(X, Y_pred, training)
with tf.device('/gpu:' + GPU0):
################################ ae loss
Y_ = tf.reshape(Y, shape=[batch_size, -1])
Y_pred_modi_ = tf.reshape(Y_pred_modi, shape=[batch_size, -1])
w = tf.placeholder(
tf.float32) # power of foreground against background
ae_loss = tf.reduce_mean(
-tf.reduce_mean(w * Y_ * tf.log(Y_pred_modi_ + 1e-8),
reduction_indices=[1]) -
tf.reduce_mean((1 - w) *
(1 - Y_) * tf.log(1 - Y_pred_modi_ + 1e-8),
reduction_indices=[1]))
sum_ae_loss = tf.summary.scalar('ae_loss', ae_loss)
################################ wgan loss
gan_g_loss = -tf.reduce_mean(XY_fake_pair)
gan_d_loss = tf.reduce_mean(XY_fake_pair) - tf.reduce_mean(
XY_real_pair)
sum_gan_g_loss = tf.summary.scalar('gan_g_loss', gan_g_loss)
sum_gan_d_loss = tf.summary.scalar('gan_d_loss', gan_d_loss)
alpha = tf.random_uniform(shape=[
batch_size, input_shape[0] * input_shape[1] * input_shape[2]
],
minval=0.0,
maxval=1.0)
Y_pred_ = tf.reshape(Y_pred, shape=[batch_size, -1])
differences_ = Y_pred_ - Y_
interpolates = Y_ + alpha * differences_
with tf.variable_scope('dis', reuse=True):
XY_fake_intep = self.dis(X, interpolates, training)
gradients = tf.gradients(XY_fake_intep, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1]))
gradient_penalty = tf.reduce_mean((slopes - 1.0)**2)
gan_d_loss += 10 * gradient_penalty
################################# ae + gan loss
gan_g_w = 5
ae_w = 100 - gan_g_w
ae_gan_g_loss = ae_w * ae_loss + gan_g_w * gan_g_loss
with tf.device('/gpu:' + GPU0):
ae_var = [
var for var in tf.trainable_variables()
if var.name.startswith('ae')
]
dis_var = [
var for var in tf.trainable_variables()
if var.name.startswith('dis')
]
ae_g_optim = tf.train.AdamOptimizer(learning_rate=lr,
beta1=0.9,
beta2=0.999,
epsilon=1e-8).minimize(
ae_gan_g_loss,
var_list=ae_var)
dis_optim = tf.train.AdamOptimizer(learning_rate=lr,
beta1=0.9,
beta2=0.999,
epsilon=1e-8).minimize(
gan_d_loss,
var_list=dis_var)
print tools.Ops.variable_count()
sum_merged = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=1)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.visible_device_list = GPU0
with tf.Session(config=config) as sess:
# if os.path.exists(self.train_models_dir):
# try:
# saver.restore(sess,self.train_models_dir+'model.cptk')
# except Exception,e:
# saver.restore(sess,'./regular/'+'model.cptk')
sum_writer_train = tf.summary.FileWriter(self.train_sum_dir,
sess.graph)
sum_write_test = tf.summary.FileWriter(self.test_sum_dir)
if os.path.isfile(self.train_models_dir +
'model.cptk.data-00000-of-00001'):
print "restoring saved model"
saver.restore(sess, self.train_models_dir + 'model.cptk')
else:
sess.run(tf.global_variables_initializer())
learning_rate_g = ori_lr * pow(power, (epoch_walked / 4))
for epoch in range(epoch_walked, 15000):
# data.shuffle_X_Y_files(label='train')
#### select data randomly each 10 epochs
if epoch % 2 == 0 and epoch > 0:
del data
gc.collect()
data = tools.Data(configure, epoch)
#### full testing
# ...
train_amount = len(data.train_numbers)
test_amount = len(data.test_numbers)
if train_amount >= test_amount and train_amount > 0 and test_amount > 0 and data.total_train_batch_num > 0 and data.total_test_seq_batch > 0:
weight_for = 0.35 * (1 - epoch * 1.0 / 15000) + 0.5
if epoch % 4 == 0:
print '********************** FULL TESTING ********************************'
time_begin = time.time()
lung_img = ST.ReadImage('./WANG_REN/lung_img.vtk')
mask_dir = "./WANG_REN/airway"
test_batch_size = batch_size
# test_data = tools.Test_data(dicom_dir,input_shape)
test_data = tools.Test_data(lung_img, input_shape,
'vtk_data')
test_data.organize_blocks()
test_mask = read_dicoms(mask_dir)
array_mask = ST.GetArrayFromImage(test_mask)
array_mask = np.transpose(array_mask, (2, 1, 0))
print "mask shape: ", np.shape(array_mask)
time1 = time.time()
block_numbers = test_data.blocks.keys()
for i in range(0, len(block_numbers), test_batch_size):
batch_numbers = []
if i + test_batch_size < len(block_numbers):
temp_input = np.zeros([
test_batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
for j in range(test_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0],
0:block_shape[1],
0:block_shape[2]] += block_array
Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
[Y_pred, Y_pred_modi, Y_pred_nosig],
feed_dict={
X: temp_input,
training: False,
w: weight_for,
threshold: upper_threshold
})
for j in range(test_batch_size):
test_data.upload_result(
batch_numbers[j],
Y_temp_modi[j, :, :, :])
else:
temp_batch_size = len(block_numbers) - i
temp_input = np.zeros([
temp_batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
for j in range(temp_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0],
0:block_shape[1],
0:block_shape[2]] += block_array
X_temp = tf.placeholder(shape=[
temp_batch_size, input_shape[0],
input_shape[1], input_shape[2]
],
dtype=tf.float32)
with tf.variable_scope('ae', reuse=True):
Y_pred_temp, Y_pred_modi_temp, Y_pred_nosig_temp = self.ae_u(
X_temp, training, temp_batch_size,
threshold)
Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
[
Y_pred_temp, Y_pred_modi_temp,
Y_pred_nosig_temp
],
feed_dict={
X_temp: temp_input,
training: False,
w: weight_for,
threshold: upper_threshold
})
for j in range(temp_batch_size):
test_data.upload_result(
batch_numbers[j],
Y_temp_modi[j, :, :, :])
test_result_array = test_data.get_result()
print "result shape: ", np.shape(test_result_array)
r_s = np.shape(test_result_array) # result shape
e_t = 10 # edge thickness
to_be_transformed = np.zeros(r_s, np.float32)
to_be_transformed[
e_t:r_s[0] - e_t, e_t:r_s[1] - e_t, e_t:r_s[2] -
e_t] += test_result_array[e_t:r_s[0] - e_t,
e_t:r_s[1] - e_t,
e_t:r_s[2] - e_t]
print np.max(to_be_transformed)
print np.min(to_be_transformed)
final_img = ST.GetImageFromArray(
np.transpose(to_be_transformed, [2, 1, 0]))
final_img.SetSpacing(test_data.space)
print "writing full testing result"
print '/usr/analyse_airway/test_result/test_result' + str(
epoch) + '.vtk'
ST.WriteImage(
final_img,
'/usr/analyse_airway/test_result/test_result' +
str(epoch) + '.vtk')
if epoch == 0:
mask_img = ST.GetImageFromArray(
np.transpose(array_mask, [2, 1, 0]))
mask_img.SetSpacing(test_data.space)
ST.WriteImage(
mask_img,
'/usr/analyse_airway/test_result/test_mask.vtk'
)
test_IOU = 2 * np.sum(
to_be_transformed * array_mask) / (
np.sum(to_be_transformed) + np.sum(array_mask))
print "IOU accuracy: ", test_IOU
time_end = time.time()
print '******************** time of full testing: ' + str(
time_end - time_begin) + 's ********************'
data.shuffle_X_Y_pairs()
total_train_batch_num = data.total_train_batch_num
# train_files=data.X_train_files
# test_files=data.X_test_files
# total_train_batch_num = 500
print "total_train_batch_num:", total_train_batch_num
for i in range(total_train_batch_num):
#### training
X_train_batch, Y_train_batch = data.load_X_Y_voxel_train_next_batch(
)
# X_train_batch, Y_train_batch = data.load_X_Y_voxel_grids_train_next_batch()
# Y_train_batch=np.reshape(Y_train_batch,[batch_size, output_shape[0], output_shape[1], output_shape[2], 1])
gan_d_loss_c, = sess.run(
[gan_d_loss],
feed_dict={
X: X_train_batch,
Y: Y_train_batch,
training: False,
w: weight_for,
threshold: upper_threshold
})
ae_loss_c, gan_g_loss_c, sum_train = sess.run(
[ae_loss, gan_g_loss, sum_merged],
feed_dict={
X: X_train_batch,
Y: Y_train_batch,
training: False,
w: weight_for,
threshold: upper_threshold
})
if epoch % 4 == 0 and epoch > 0 and i == 0:
learning_rate_g = learning_rate_g * power
sess.run(
[ae_g_optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
if epoch <= 5:
sess.run(
[dis_optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
elif epoch <= 20:
sess.run(
[dis_optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
else:
sess.run(
[dis_optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
sum_writer_train.add_summary(
sum_train, epoch * total_train_batch_num + i)
if i % 2 == 0:
print "epoch:", epoch, " i:", i, " train ae loss:", ae_loss_c, " gan g loss:", gan_g_loss_c, " gan d loss:", gan_d_loss_c, " learning rate: ", learning_rate_g
#### testing
if i % 20 == 0 and epoch % 1 == 0:
try:
X_test_batch, Y_test_batch = data.load_X_Y_voxel_test_next_batch(
fix_sample=False)
# Y_test_batch = np.reshape(Y_test_batch,[batch_size, output_shape[0], output_shape[1], output_shape[2], 1])
ae_loss_t,gan_g_loss_t,gan_d_loss_t, Y_test_pred,Y_test_modi, Y_test_pred_nosig= \
sess.run([ae_loss, gan_g_loss,gan_d_loss, Y_pred,Y_pred_modi,Y_pred_nosig],feed_dict={X: X_test_batch, threshold:upper_threshold, Y: Y_test_batch,training:False, w: weight_for})
predict_result = np.float32(Y_test_modi > 0.01)
predict_result = np.reshape(
predict_result, [
batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
# Foreground
# if np.sum(Y_test_batch)>0:
# accuracy_for = np.sum(predict_result*Y_test_batch)/np.sum(Y_test_batch)
# Background
# accuracy_bac = np.sum((1-predict_result)*(1-Y_test_batch))/(np.sum(1-Y_test_batch))
# IOU
predict_probablity = np.float32(
(Y_test_modi - 0.01) > 0)
predict_probablity = np.reshape(
predict_probablity, [
batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
accuracy = 2 * np.sum(
np.abs(predict_probablity *
Y_test_batch)) / np.sum(
np.abs(predict_result) +
np.abs(Y_test_batch))
# if epoch%30==0 and epoch>0:
# to_save = {'X_test': X_test_batch, 'Y_test_pred': Y_test_pred,'Y_test_true': Y_test_batch}
# scipy.io.savemat(self.test_results_dir + 'X_Y_pred_' + str(epoch).zfill(2) + '_' + str(i).zfill(4) + '.mat', to_save, do_compression=True)
print "epoch:", epoch, " i:", "\nIOU accuracy: ", accuracy, "\ntest ae loss:", ae_loss_t, " gan g loss:", gan_g_loss_t, " gan d loss:", gan_d_loss_t
if accuracy > best_acc:
saver.save(
sess,
save_path=self.train_models_dir +
'model.cptk')
print "epoch:", epoch, " i:", i, "best model saved!"
best_acc = accuracy
except Exception, e:
print e
#### model saving
if i % 30 == 0 and epoch % 1 == 0:
# regular_train_dir = "./regular/"
# if not os.path.exists(regular_train_dir):
# os.makedirs(regular_train_dir)
saver.save(sess,
save_path=self.train_models_dir +
'model.cptk')
print "epoch:", epoch, " i:", i, "regular model saved!"
else:
print "bad data , next epoch", epoch
def test(self, dicom_dir):
flags = self.FLAGS
block_shape = self.block_shape
batch_size_test = self.batch_size_test
data_type = "dicom_data"
X = tf.placeholder(dtype=tf.float32,
shape=[
batch_size_test, block_shape[0], block_shape[1],
block_shape[2]
])
training = tf.placeholder(tf.bool)
artery_pred, artery_sig = self.Dense_Net_Test(X, training,
flags.batch_size_test,
flags.accept_threshold)
artery_pred = tf.reshape(
artery_pred,
[batch_size_test, block_shape[0], block_shape[1], block_shape[2]])
# binary predict mask
artery_pred_mask = tf.cast((artery_pred > 0.01), tf.float32)
saver = tf.train.Saver(max_to_keep=1)
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
# load variables if saved before
if len(os.listdir(self.train_models_dir)) > 0:
print "load saved model"
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
saver.restore(sess,
self.train_models_dir + "train_models.ckpt")
else:
print "no model detected from %s" % (self.train_models_dir)
exit(1)
test_data = tools.Test_data(dicom_dir, block_shape, data_type)
test_data.organize_blocks()
block_numbers = test_data.blocks.keys()
blocks_num = len(block_numbers)
print "block count: ", blocks_num
time1 = time.time()
sys.stdout.write("\r>>>deep learning calculating : %f" % (0.0) +
"%")
sys.stdout.flush()
for i in range(0, blocks_num, batch_size_test):
batch_numbers = []
if i + batch_size_test < blocks_num:
temp_batch_size = batch_size_test
else:
temp_batch_size = blocks_num - i
temp_input = np.zeros([
batch_size_test, block_shape[0], block_shape[1],
block_shape[2]
])
for j in range(temp_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
data_block_shape = np.shape(block_array)
temp_input[j, 0:data_block_shape[0], 0:data_block_shape[1],
0:data_block_shape[2]] += block_array
artery_predict = sess.run(artery_pred_mask,
feed_dict={
X: temp_input,
training: False
})
for j in range(temp_batch_size):
test_data.upload_result(batch_numbers[j],
artery_predict[j, :, :, :])
if (i) % (batch_size_test * 10) == 0:
sys.stdout.write("\r>>>deep learning calculating : %f" %
((1.0 * i) * 100 / blocks_num) + "%")
sys.stdout.flush()
sys.stdout.write("\r>>>deep learning calculating : %f" % (100.0) +
"%")
sys.stdout.flush()
time2 = time.time()
print "\ndeep learning time consume : ", str(time2 - time1)
time3 = time.time()
test_result_array = test_data.get_result()
print "result shape: ", np.shape(test_result_array)
r_s = np.shape(test_result_array) # result shape
e_t = 10 # edge thickness
to_be_transformed = np.zeros(r_s, np.float32)
to_be_transformed[e_t:r_s[0] - e_t, e_t:r_s[1] - e_t, 0:r_s[2] -
e_t] += test_result_array[e_t:r_s[0] - e_t,
e_t:r_s[1] - e_t,
0:r_s[2] - e_t]
print "maximum value in mask: ", np.max(to_be_transformed)
print "minimum value in mask: ", np.min(to_be_transformed)
final_img = ST.GetImageFromArray(
np.transpose(np.int8(to_be_transformed), [2, 1, 0]))
final_img.SetSpacing(test_data.space)
time4 = time.time()
print "post processing time consume : ", str(time4 - time3)
print "writing final testing result"
print './test_result/test_result_final.vtk'
ST.WriteImage(final_img, './test_result/test_result_final.vtk')
return final_img
def train(self, configure):
# data
data = tools.Data(configure, epoch_walked / re_example_epoch)
# network
X = tf.placeholder(
shape=[batch_size, input_shape[0], input_shape[1], input_shape[2]],
dtype=tf.float32)
Y = tf.placeholder(shape=[
batch_size, output_shape[0], output_shape[1], output_shape[2]
],
dtype=tf.float32)
lr = tf.placeholder(tf.float32)
training = tf.placeholder(tf.bool)
threshold = tf.placeholder(tf.float32)
with tf.variable_scope('segmenter'):
Y_pred, Y_pred_modi, Y_pred_nosig = self.ae_u(
X, training, batch_size, threshold)
# loss function
Y_ = tf.reshape(Y, shape=[batch_size, -1])
Y_pred_modi_ = tf.reshape(Y_pred_modi, shape=[batch_size, -1])
w = tf.placeholder(tf.float32) # foreground weight
cross_loss = tf.reduce_mean(
-tf.reduce_mean(w * Y_ * tf.log(Y_pred_modi_ + 1e-8),
reduction_indices=[1]) -
tf.reduce_mean((1 - w) *
(1 - Y_) * tf.log(1 - Y_pred_modi_ + 1e-8),
reduction_indices=[1]))
loss_sum = tf.summary.scalar("cross entropy", cross_loss)
# trainers
optim = tf.train.AdamOptimizer(learning_rate=lr,
beta1=0.9,
beta2=0.999,
epsilon=1e-8).minimize(cross_loss)
# accuracy
block_acc = tf.placeholder(tf.float32)
total_acc = tf.placeholder(tf.float32)
train_sum = tf.summary.scalar("train_block_accuracy", block_acc)
test_sum = tf.summary.scalar("total_test_accuracy", total_acc)
train_merge_op = tf.summary.merge([train_sum, loss_sum])
test_merge_op = tf.summary.merge([test_sum])
saver = tf.train.Saver(max_to_keep=1)
# config = tf.ConfigProto(allow_soft_placement=True)
# config.gpu_options.visible_device_list = GPU0
with tf.Session() as sess:
# define tensorboard writer
sum_writer_train = tf.summary.FileWriter(self.train_sum_dir,
sess.graph)
sum_write_test = tf.summary.FileWriter(self.test_sum_dir,
sess.graph)
# load model data if pre-trained
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
if os.path.isfile(self.train_models_dir +
'model.cptk.data-00000-of-00001'):
print "restoring saved model"
saver.restore(sess, self.train_models_dir + 'model.cptk')
learning_rate_g = ori_lr * pow(power, (epoch_walked / decay_step))
# start training loop
global_step = step_walked
for epoch in range(epoch_walked, MAX_EPOCH):
if epoch % re_example_epoch == 0 and epoch > 0:
del data
gc.collect()
data = tools.Data(configure, epoch / re_example_epoch)
train_amount = len(data.train_numbers)
test_amount = len(data.test_numbers)
if train_amount >= test_amount and train_amount > 0 and test_amount > 0 and data.total_train_batch_num > 0 and data.total_test_seq_batch > 0:
# actual foreground weight
weight_for = 0.5 + (1 - 1.0 * epoch / MAX_EPOCH) * 0.35
if epoch % total_test_epoch == 0 and epoch > 0:
print '********************** FULL TESTING ********************************'
time_begin = time.time()
origin_dir = read_dicoms(test_dir + "original1")
mask_dir = test_dir + "artery"
test_batch_size = batch_size
# test_data = tools.Test_data(dicom_dir,input_shape)
test_data = tools.Test_data(origin_dir, input_shape,
'vtk_data')
test_data.organize_blocks()
test_mask = read_dicoms(mask_dir)
array_mask = ST.GetArrayFromImage(test_mask)
array_mask = np.transpose(array_mask, (2, 1, 0))
print "mask shape: ", np.shape(array_mask)
block_numbers = test_data.blocks.keys()
for i in range(0, len(block_numbers), test_batch_size):
batch_numbers = []
if i + test_batch_size < len(block_numbers):
temp_input = np.zeros([
test_batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
for j in range(test_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0],
0:block_shape[1],
0:block_shape[2]] += block_array
Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
[Y_pred, Y_pred_modi, Y_pred_nosig],
feed_dict={
X:
temp_input,
training:
False,
w:
weight_for,
threshold:
upper_threshold + test_extra_threshold
})
for j in range(test_batch_size):
test_data.upload_result(
batch_numbers[j],
Y_temp_modi[j, :, :, :])
else:
temp_batch_size = len(block_numbers) - i
temp_input = np.zeros([
temp_batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
for j in range(temp_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0],
0:block_shape[1],
0:block_shape[2]] += block_array
X_temp = tf.placeholder(shape=[
temp_batch_size, input_shape[0],
input_shape[1], input_shape[2]
],
dtype=tf.float32)
with tf.variable_scope('segmenter',
reuse=True):
Y_pred_temp, Y_pred_modi_temp, Y_pred_nosig_temp = self.ae_u(
X_temp, training, temp_batch_size,
threshold)
Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
[
Y_pred_temp, Y_pred_modi_temp,
Y_pred_nosig_temp
],
feed_dict={
X_temp:
temp_input,
training:
False,
w:
weight_for,
threshold:
upper_threshold + test_extra_threshold
})
for j in range(temp_batch_size):
test_data.upload_result(
batch_numbers[j],
Y_temp_modi[j, :, :, :])
test_result_array = test_data.get_result()
print "result shape: ", np.shape(test_result_array)
to_be_transformed = self.post_process(
test_result_array)
if epoch % output_epoch == 0:
self.output_img(to_be_transformed, test_data.space,
epoch)
if epoch == 0:
mask_img = ST.GetImageFromArray(
np.transpose(array_mask, [2, 1, 0]))
mask_img.SetSpacing(test_data.space)
ST.WriteImage(mask_img,
'./test_result/test_mask.vtk')
test_IOU = 2 * np.sum(
to_be_transformed * array_mask) / (
np.sum(to_be_transformed) + np.sum(array_mask))
test_summary = sess.run(
test_merge_op, feed_dict={total_acc: test_IOU})
sum_write_test.add_summary(test_summary,
global_step=epoch)
print "IOU accuracy: ", test_IOU
time_end = time.time()
print '******************** time of full testing: ' + str(
time_end - time_begin) + 's ********************'
data.shuffle_X_Y_pairs()
total_train_batch_num = data.total_train_batch_num
print "total_train_batch_num:", total_train_batch_num
for i in range(total_train_batch_num):
X_train_batch, Y_train_batch = data.load_X_Y_voxel_train_next_batch(
)
# calculate loss value
# print "calculate begin"
loss_c = sess.run(
[cross_loss],
feed_dict={
X: X_train_batch,
Y: Y_train_batch,
training: False,
w: weight_for,
threshold: upper_threshold
})
# print "calculate ended"
if epoch % decay_step == 0 and epoch > epoch_walked and i == 0:
learning_rate_g = learning_rate_g * power
sess.run(
[optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
# print "training ended"
global_step += 1
# output some results
if i % show_step == 0:
print "epoch:", epoch, " i:", i, " train loss:", loss_c, " gan g loss:", learning_rate_g
if i % block_test_step == 0 and epoch % 1 == 0:
try:
X_test_batch, Y_test_batch = data.load_X_Y_voxel_test_next_batch(
fix_sample=False)
Y_test_pred, Y_test_modi, Y_test_pred_nosig ,loss_t= \
sess.run([ Y_pred, Y_pred_modi, Y_pred_nosig,cross_loss],
feed_dict={X: X_test_batch,
threshold: upper_threshold + test_extra_threshold,
Y: Y_test_batch, training: False, w: weight_for})
predict_result = np.float32(Y_test_modi > 0.01)
predict_result = np.reshape(
predict_result, [
batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
print np.max(Y_test_pred)
print np.min(Y_test_pred)
# IOU
predict_probablity = np.float32(
(Y_test_modi - 0.01) > 0)
predict_probablity = np.reshape(
predict_probablity, [
batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
accuracy = 2 * np.sum(
np.abs(predict_probablity *
Y_test_batch)) / np.sum(
np.abs(predict_result) +
np.abs(Y_test_batch))
print "epoch:", epoch, " global step: ", global_step, "\nIOU accuracy: ", accuracy, "\ntest ae loss:", loss_t
print "weight of foreground : ", weight_for
print "upper threshold of testing", (
upper_threshold + test_extra_threshold)
train_summary = sess.run(
train_merge_op,
feed_dict={
block_acc: accuracy,
X: X_test_batch,
threshold:
upper_threshold + test_extra_threshold,
Y: Y_test_batch,
training: False,
w: weight_for
})
sum_writer_train.add_summary(
train_summary, global_step=global_step)
except Exception, e:
print e
#### model saving
if i % model_save_step == 0 and epoch % 1 == 0:
saver.save(sess,
save_path=self.train_models_dir +
'model.cptk')
print "epoch:", epoch, " i:", i, "regular model saved!"
else:
print "bad data , next epoch", epoch
import os import shutil import tensorflow as tf import scipy.io import tools import numpy as np import time import test import SimpleITK as ST from dicom_read import read_dicoms import gc input_shape = [64, 64, 128] test_dir = './FU_LI_JUN/' origin_dir = read_dicoms(test_dir + "original1") test_data = tools.Test_data(origin_dir, input_shape, 'vtk_data') test_data.output_origin() print "end"
def train(self):
flags = self.FLAGS
block_shape = self.block_shape
record_dir = self.record_dir
record_dir_test = self.record_dir_test
batch_size_train = self.batch_size_train
batch_size_test = self.batch_size_test
test_step = self.test_step
threashold = flags.accept_threshold
LEARNING_RATE_BASE = flags.training_rate_base
LEARNING_RATE_DECAY = flags.training_rate_decay
weight_vec = tf.constant([
flags.airway_weight, flags.artery_weight, flags.back_ground_weight
], tf.float32)
X = tf.placeholder(dtype=tf.float32,
shape=[
batch_size_train, block_shape[0],
block_shape[1], block_shape[2]
])
training = tf.placeholder(tf.bool)
with tf.variable_scope('network'):
seg_pred = self.Dense_Net(X, training, flags.batch_size_train,
flags.accept_threshold)
# lost function
'''
lable vector: [airway,artery,background]
'''
lables = tf.placeholder(dtype=tf.float32,
shape=[
batch_size_train, block_shape[0],
block_shape[1], block_shape[2], 3
])
weight_map = tf.reduce_sum(tf.multiply(lables, weight_vec), 4)
loss_origin = tf.nn.softmax_cross_entropy_with_logits(logits=seg_pred,
labels=lables)
loss_weighted = weight_map * loss_origin
loss = tf.reduce_mean(loss_weighted)
tf.summary.scalar('loss', loss)
# accuracy
# predict_softmax = tf.nn.softmax(seg_pred)
pred_map = tf.argmax(seg_pred, axis=-1)
pred_map_bool = tf.equal(pred_map, 1)
artery_pred_mask = tf.cast(pred_map_bool, tf.float32)
artery_lable = tf.cast(lables[:, :, :, :, 0], tf.float32)
artery_acc = 2 * tf.reduce_sum(artery_lable * artery_pred_mask) / (
tf.reduce_sum(artery_lable + artery_pred_mask))
tf.summary.scalar('airway_block_acc', artery_acc)
# data part
records = ut.get_records(record_dir)
records_processor = TF_Records(records, block_shape)
single_blocks = records_processor.read_records()
queue = tf.RandomShuffleQueue(capacity=8,
min_after_dequeue=4,
dtypes=(
single_blocks['airway'].dtype,
single_blocks['artery'].dtype,
single_blocks['lung'].dtype,
single_blocks['original'].dtype,
))
enqueue_op = queue.enqueue((
single_blocks['airway'],
single_blocks['artery'],
single_blocks['lung'],
single_blocks['original'],
))
(airway_block, artery_block, lung_block,
original_block) = queue.dequeue()
qr = tf.train.QueueRunner(queue, [enqueue_op] * 2)
# test data part
records_test = ut.get_records(record_dir_test)
records_processor_test = TF_Records(records_test, block_shape)
single_blocks_test = records_processor_test.read_records()
queue_test = tf.RandomShuffleQueue(
capacity=8,
min_after_dequeue=4,
dtypes=(
single_blocks_test['airway'].dtype,
single_blocks_test['artery'].dtype,
single_blocks_test['lung'].dtype,
single_blocks_test['original'].dtype,
))
enqueue_op_test = queue_test.enqueue((
single_blocks_test['airway'],
single_blocks_test['artery'],
single_blocks_test['lung'],
single_blocks_test['original'],
))
(airway_block_test, artery_block_test, lung_block_test,
original_block_test) = queue_test.dequeue()
qr_test = tf.train.QueueRunner(queue, [enqueue_op_test] * 2)
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.maximum(
tf.train.exponential_decay(LEARNING_RATE_BASE,
global_step,
13500 * 5 / flags.batch_size_train,
LEARNING_RATE_DECAY,
staircase=True), 1e-9)
train_op = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.9,
beta2=0.999,
epsilon=1e-8).minimize(
loss, global_step)
# merge operation for tensorboard summary
merge_summary_op = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=1)
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
# load variables if saved before
if len(os.listdir(self.train_models_dir)) > 0:
print "load saved model"
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
saver.restore(sess,
self.train_models_dir + "train_models.ckpt")
else:
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
# coord for the reading threads
coord = tf.train.Coordinator()
enqueue_threads = qr.create_threads(sess, coord=coord, start=True)
enqueue_threads_test = qr_test.create_threads(sess,
coord=coord,
start=True)
tf.train.start_queue_runners(sess=sess)
summary_writer_test = tf.summary.FileWriter(
self.test_sum_dir, sess.graph)
summary_writer_train = tf.summary.FileWriter(
self.train_sum_dir, sess.graph)
# main train loop
# for i in range(flags.max_iteration_num):
for i in range(flags.max_iteration_num):
# organize a batch of data for training
lable_np = np.zeros([
batch_size_train, block_shape[0], block_shape[1],
block_shape[2], 3
], np.int16)
original_np = np.zeros([
batch_size_train, block_shape[0], block_shape[1],
block_shape[2]
], np.int16)
# store values into data block
for m in range(flags.batch_size_train):
'''
lable vector: [airway,artery,background]
'''
artery_data, airway_data, original_data = \
sess.run([artery_block, airway_block, original_block])
airway_array = airway_data
artery_array = artery_data
back_ground_array = np.int16((airway_array +
artery_array) == 0)
check_array = airway_array + artery_array + back_ground_array
while not np.max(check_array) == np.min(check_array) == 1:
artery_data, airway_data, original_data = \
sess.run([artery_block, airway_block, original_block])
airway_array = airway_data
artery_array = artery_data
back_ground_array = np.int16((airway_array +
artery_array) == 0)
check_array = airway_array + artery_array + back_ground_array
lable_np[m, :, :, :, 0] += airway_array
lable_np[m, :, :, :, 1] += artery_array
lable_np[m, :, :, :, 2] += back_ground_array
original_np[m, :, :, :] += original_data
train_, step_num = sess.run([train_op, global_step],
feed_dict={
X: original_np,
lables: lable_np,
training: True
})
if step_num % flags.full_test_step == 0:
# full testing
print "****************************full testing******************************"
data_type = "dicom_data"
test_dicom_dir = '/opt/Multi-Task-data-process/multi_task_data_test/FU_LI_JUN/original1'
test_mask_dir = '/opt/Multi-Task-data-process/multi_task_data_test/FU_LI_JUN/artery'
test_mask = ut.read_dicoms(test_mask_dir)
test_mask_array = np.transpose(
ST.GetArrayFromImage(test_mask), [2, 1, 0])
test_data = tools.Test_data(test_dicom_dir, block_shape,
data_type)
test_data.organize_blocks()
block_numbers = test_data.blocks.keys()
blocks_num = len(block_numbers)
print "block count: ", blocks_num
time1 = time.time()
sys.stdout.write("\r>>>deep learning calculating : %f" %
(0.0) + "%")
sys.stdout.flush()
for m in range(0, blocks_num, batch_size_train):
batch_numbers = []
if m + batch_size_train < blocks_num:
temp_batch_size = batch_size_train
else:
temp_batch_size = blocks_num - m
temp_input = np.zeros([
batch_size_train, block_shape[0], block_shape[1],
block_shape[2]
])
for j in range(temp_batch_size):
temp_num = block_numbers[m + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
data_block_shape = np.shape(block_array)
temp_input[j, 0:data_block_shape[0],
0:data_block_shape[1],
0:data_block_shape[2]] += block_array
artery_predict = sess.run(artery_pred_mask,
feed_dict={
X: temp_input,
training: False
})
for j in range(temp_batch_size):
test_data.upload_result(batch_numbers[j],
artery_predict[j, :, :, :])
if (m) % (batch_size_train * 10) == 0:
sys.stdout.write(
"\r>>>deep learning calculating : %f" %
((1.0 * m) * 100 / blocks_num) + "%")
sys.stdout.flush()
sys.stdout.write("\r>>>deep learning calculating : %f" %
(100.0) + "%")
sys.stdout.flush()
time2 = time.time()
print "\ndeep learning time consume : ", str(time2 - time1)
time3 = time.time()
test_result_array = test_data.get_result()
test_result_array = np.float32(test_result_array >= 2)
print "result shape: ", np.shape(test_result_array)
r_s = np.shape(test_result_array) # result shape
e_t = 10 # edge thickness
to_be_transformed = np.zeros(r_s, np.float32)
to_be_transformed[e_t:r_s[0] - e_t, e_t:r_s[1] - e_t,
0:r_s[2] -
e_t] += test_result_array[e_t:r_s[0] -
e_t,
e_t:r_s[1] -
e_t,
0:r_s[2] - e_t]
print "maximum value in mask: ", np.max(to_be_transformed)
print "minimum value in mask: ", np.min(to_be_transformed)
final_img = ST.GetImageFromArray(
np.transpose(to_be_transformed, [2, 1, 0]))
final_img.SetSpacing(test_data.space)
time4 = time.time()
print "post processing time consume : ", str(time4 - time3)
print "writing final testing result"
if not os.path.exists('./test_result'):
os.makedirs('./test_result')
print './test_result/test_result_' + str(step_num) + '.vtk'
ST.WriteImage(
final_img,
'./test_result/test_result_' + str(step_num) + '.vtk')
total_accuracy = 2 * np.sum(
1.0 * test_mask_array * to_be_transformed) / np.sum(
1.0 * (test_mask_array + to_be_transformed))
print "total IOU accuracy : ", total_accuracy
if i == 0:
mask_img = ST.GetImageFromArray(
np.transpose(test_mask_array, [2, 1, 0]))
mask_img.SetSpacing(test_data.space)
ST.WriteImage(mask_img, './test_result/mask_img.vtk')
print "***********************full testing end*******************************"
if i % 10 == 0:
sum_train,\
l_val \
= sess.run([merge_summary_op,
loss],
feed_dict={X: original_np,
lables: lable_np, training: False})
summary_writer_train.add_summary(sum_train,
global_step=int(step_num))
print "train :\nstep %d , loss = %f\n =====================" \
% (int(step_num), l_val)
if i % test_step == 0 and i > 0:
lable_np_test = np.zeros([
batch_size_train, block_shape[0], block_shape[1],
block_shape[2], 3
], np.int16)
original_np_test = np.zeros([
batch_size_train, block_shape[0], block_shape[1],
block_shape[2]
], np.int16)
for m in range(flags.batch_size_train):
'''
lable vector: [airway,artery,background]
'''
artery_data, airway_data, original_data = \
sess.run([artery_block_test, airway_block_test, original_block_test])
airway_array = airway_data
artery_array = artery_data
back_ground_array = np.int16((airway_array +
artery_array) == 0)
check_array = airway_array + artery_array + back_ground_array
while not np.max(check_array) == np.min(
check_array) == 1:
artery_data, airway_data, original_data = \
sess.run([artery_block, airway_block, original_block])
airway_array = airway_data
artery_array = artery_data
back_ground_array = np.int16((airway_array +
artery_array) == 0)
check_array = airway_array + artery_array + back_ground_array
lable_np_test[m, :, :, :, 0] += airway_array
lable_np_test[m, :, :, :, 1] += artery_array
lable_np_test[m, :, :, :, 2] += back_ground_array
original_np_test[m, :, :, :] += original_data
sum_test, accuracy_artery, l_val, predict_array = \
sess.run([merge_summary_op,artery_acc,loss,pred_map],
feed_dict={X: original_np_test,lables: lable_np_test, training: False})
summary_writer_test.add_summary(sum_test,
global_step=int(step_num))
print "\ntest :\nstep %d , artery loss = %f \n\t artery block accuracy = %f\n=====================" \
% (int(step_num), l_val, accuracy_artery)
print "artery percentage : ", str(
np.float32(
np.sum(np.float32(lable_np_test[:, :, :, :, 1])) /
(flags.batch_size_train * block_shape[0] *
block_shape[1] * block_shape[2])))
# print "prediction of airway : maximum = ",np.max(airway_np_sig)," minimum = ",np.min(airway_np_sig)
print "prediction : maximum = ", np.max(
predict_array), " minimum = ", np.min(predict_array)
if i % 100 == 0:
saver.save(sess,
self.train_models_dir + "train_models.ckpt")
print "regular model saved! step count : ", step_num
coord.request_stop()
coord.join(enqueue_threads)
coord.join(enqueue_threads_test)
def full_testing(self, sess, epoch):
print '********************** FULL TESTING ********************************'
time_begin = time.time()
origin_data = read_dicoms(test_dir + "original1")
mask_dir = test_dir + "artery"
test_batch_size = batch_size
test_data = tools.Test_data(origin_data, input_shape, 'vtk_data')
test_data.organize_blocks()
test_mask = read_dicoms(mask_dir)
array_mask = ST.GetArrayFromImage(test_mask)
array_mask = np.transpose(array_mask, (2, 1, 0))
print "mask shape: ", np.shape(array_mask)
block_numbers = test_data.blocks.keys()
for i in range(0, len(block_numbers), test_batch_size):
batch_numbers = []
if i + test_batch_size < len(block_numbers):
temp_input = np.zeros([
test_batch_size, input_shape[0], input_shape[1],
input_shape[2]
])
for j in range(test_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0], 0:block_shape[1],
0:block_shape[2]] += block_array
pred_unsoft, softmax_pred, argmax_label = \
sess.run([self.pred_unsoft, self.softmax_pred, self.argmax_label],
feed_dict={self.X: temp_input,
self.training: False})
for j in range(test_batch_size):
test_data.upload_result_multiclass(
batch_numbers[j], argmax_label[j, :, :, :], mask_type)
else:
temp_batch_size = len(block_numbers) - i
temp_input = np.zeros([
temp_batch_size, input_shape[0], input_shape[1],
input_shape[2]
])
for j in range(temp_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0], 0:block_shape[1],
0:block_shape[2]] += block_array
X_temp = tf.placeholder(shape=[
temp_batch_size, input_shape[0], input_shape[1],
input_shape[2]
],
dtype=tf.float32)
with tf.variable_scope("segment", reuse=True):
temp_unsoft, softmax_temp, argmax_temp = self.Segmentor(
X_temp, self.training, batch_size)
pred_unsoft_temp, softmax_pred_temp, argmax_label_temp = \
sess.run([temp_unsoft, softmax_temp,argmax_temp],feed_dict={X_temp:temp_input,
self.training:False})
for j in range(temp_batch_size):
test_data.upload_result_multiclass(
batch_numbers[j], argmax_label_temp[j, :, :, :],
mask_type)
test_result_array = test_data.get_result_()
print "result shape: ", np.shape(test_result_array)
to_be_transformed = self.post_process(test_result_array)
if epoch == 0:
mask_img = ST.GetImageFromArray(np.transpose(
array_mask, [2, 1, 0]))
mask_img.SetSpacing(test_data.space)
ST.WriteImage(mask_img, './test_result/test_mask.vtk')
test_IOU = 2 * np.sum(to_be_transformed * array_mask) / (
np.sum(to_be_transformed) + np.sum(array_mask))
test_summary = sess.run(self.test_merge_op,
feed_dict={self.total_acc: test_IOU})
self.sum_write_test.add_summary(test_summary, global_step=epoch)
print "IOU accuracy: ", test_IOU
time_end = time.time()
print '******************** time of full testing: ' + str(
time_end - time_begin) + 's ********************'
