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model.py
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model.py
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from __future__ import division
import os
import time
from glob import glob
import cv2
import scipy.ndimage
from ops import *
from utils import *
from seg_eval import *
class cgan_unet_xy(object):
""" Implementation of 3D U-net"""
def __init__(self, sess, param_set):
self.sess = sess
self.phase = param_set['phase']
self.batch_size = param_set['batch_size']
self.inputI_size = param_set['inputI_size']
self.inputI_chn = param_set['inputI_chn']
self.outputI_size = param_set['outputI_size']
self.output_chn = param_set['output_chn']
self.resize_r = param_set['resize_r']
self.pad_w = param_set['pad_w']
self.traindata_dir = param_set['traindata_dir']
self.chkpoint_dir = param_set['chkpoint_dir']
self.lr = param_set['learning_rate']
self.beta1 = param_set['beta1']
self.epoch = param_set['epoch']
self.model_name = param_set['model_name']
self.save_intval = param_set['save_intval']
self.testdata_dir = param_set['testdata_dir']
self.labeling_dir = param_set['labeling_dir']
self.ovlp_ita = param_set['ovlp_ita']
self.rename_map = param_set['rename_map']
self.rename_map = [int(s) for s in self.rename_map.split(',')]
self.L1_lambda = param_set['L1_lambda']
# build model graph
self.build_cgan_model()
# build 3d unet based cgan graph
def build_cgan_model(self):
# input
self.real_I = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, self.inputI_size, self.inputI_size, self.inputI_size, self.inputI_chn], name='inputI')
self.real_label = tf.placeholder(dtype=tf.int32, shape=[self.batch_size, self.inputI_size, self.inputI_size, self.inputI_size], name='target')
self.real_label_flt = tf.cast(self.real_label, dtype=tf.float32, name='target_float')
self.real_label_chn = tf.reshape(self.real_label_flt, [self.batch_size, self.inputI_size, self.inputI_size, self.inputI_size, 1], name='target_flt_reshape')
# unet as generator
# self.main_prob, self.fake_label, self.aux0_prob, self.aux1_prob = self.unet_3D_model(self.real_I)
self.main_prob, self.fake_label, self.aux0_prob, self.aux1_prob, self.aux2_prob = self.unet_3D_model(self.real_I)
self.fake_label_flt = tf.cast(self.fake_label, dtype=tf.float32, name='fake_label_float')
self.fake_label_chn = tf.reshape(self.fake_label_flt, [self.batch_size, self.inputI_size, self.inputI_size, self.inputI_size, 1], name='fake_label_reshape')
# build pairs
self.real_pair = tf.concat([self.real_I, self.real_label_chn], axis=4)
self.fake_pair = tf.concat([self.real_I, self.fake_label_chn], axis=4)
# discrimination
self.D_r, self.D_r_logits = self.discriminator(self.real_pair, reuse=False)
self.D_f, self.D_f_logits = self.discriminator(self.fake_pair, reuse=True)
# ====== loss
# === generator
self.g2d_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(self.D_f), logits=self.D_f_logits)) + self.L1_lambda * tf.reduce_mean(tf.abs(self.real_label_flt - self.fake_label_flt))
# unet loss with deep supervision
self.g2g_main_loss = tf.reduce_mean(input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.real_label, logits=self.main_prob, name='main_loss'))
self.g2g_aux0_loss = tf.reduce_mean(input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.real_label, logits=self.aux0_prob, name='aux0_loss'))
self.g2g_aux1_loss = tf.reduce_mean(input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.real_label, logits=self.aux1_prob, name='aux1_loss'))
self.g2g_loss = self.g2g_main_loss + tf.constant(0.5)*self.g2g_aux0_loss + tf.constant(0.5)*self.g2g_aux1_loss
self.g_loss = self.g2d_loss + self.g2g_loss
# === discriminator
self.d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(self.D_r), logits=self.D_r_logits))
self.d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(self.D_f), logits=self.D_f_logits))
self.d_loss = self.d_loss_real + self.d_loss_fake
t_vars = tf.trainable_variables()
# self.g_vars = [var for var in t_vars if 'unet3D_model' in var.name and 'gamma:0' not in var.name]
self.g_vars = [var for var in t_vars if 'discriminator' not in var.name]
self.d_vars = [var for var in t_vars if 'd_' in var.name and 'discriminator' in var.name]
# model saver
self.saver = tf.train.Saver()
#
self.saver_unet = tf.train.Saver(self.g_vars)
# 3D unet graph
def unet_3D_model(self, inputI):
"""3D U-net"""
phase_flag = (self.phase =='train')
concat_dim = 4
# with tf.variable_scope("unet3D_model") as scope:
# down-sampling path
# compute down-sample path in gpu0
with tf.device("/gpu:0"):
# conv1_1 = conv_bn_relu(input=inputI, output_chn=64, kernel_size=3, stride=1, use_bias=False, is_training=phase_flag, name='Conv1')
conv1_1 = conv3d(input=inputI, output_chn=64, kernel_size=3, stride=1, use_bias=False, name='conv1')
conv1_bn = tf.contrib.layers.batch_norm(conv1_1, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True, is_training=phase_flag, scope="conv1_batch_norm")
conv1_relu = tf.nn.relu(conv1_bn, name='conv1_relu')
pool1 = tf.layers.max_pooling3d(inputs=conv1_relu, pool_size=2, strides=2, name='pool1')
#
# conv2_1 = conv_bn_relu(input=pool1, output_chn=128, kernel_size=3, stride=1, use_bias=False, is_training=phase_flag, name='Conv2')
conv2_1 = conv3d(input=pool1, output_chn=128, kernel_size=3, stride=1, use_bias=False, name='conv2')
conv2_bn = tf.contrib.layers.batch_norm(conv2_1, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True, is_training=phase_flag, scope="conv2_batch_norm")
conv2_relu = tf.nn.relu(conv2_bn, name='conv2_relu')
pool2 = tf.layers.max_pooling3d(inputs=conv2_relu, pool_size=2, strides=2, name='pool2')
#
# conv3_1 = conv_bn_relu(input=pool2, output_chn=256, kernel_size=3, stride=1, use_bias=False, is_training=phase_flag, name='Conv3a')
conv3_1 = conv3d(input=pool2, output_chn=256, kernel_size=3, stride=1, use_bias=False, name='conv3a')
conv3_1_bn = tf.contrib.layers.batch_norm(conv3_1, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True, is_training=phase_flag, scope="conv3_1_batch_norm")
conv3_1_relu = tf.nn.relu(conv3_1_bn, name='conv3_1_relu')
# conv3_2 = conv_bn_relu(input=conv3_1, output_chn=256, kernel_size=3, stride=1, use_bias=False, is_training=phase_flag, name='Conv3b')
conv3_2 = conv3d(input=conv3_1_relu, output_chn=256, kernel_size=3, stride=1, use_bias=False, name='conv3b')
conv3_2_bn = tf.contrib.layers.batch_norm(conv3_2, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True, is_training=phase_flag, scope="conv3_2_batch_norm")
conv3_2_relu = tf.nn.relu(conv3_2_bn, name='conv3_2_relu')
pool3 = tf.layers.max_pooling3d(inputs=conv3_2_relu, pool_size=2, strides=2, name='pool3')
#
# conv4_1 = conv_bn_relu(input=pool3, output_chn=512, kernel_size=3, stride=1, use_bias=False, is_training=phase_flag, name='Conv4a')
conv4_1 = conv3d(input=pool3, output_chn=512, kernel_size=3, stride=1, use_bias=False, name='conv4a')
conv4_1_bn = tf.contrib.layers.batch_norm(conv4_1, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True, is_training=phase_flag, scope="conv4_1_batch_norm")
conv4_1_relu = tf.nn.relu(conv4_1_bn, name='conv4_1_relu')
# conv4_2 = conv_bn_relu(input=conv4_1, output_chn=512, kernel_size=3, stride=1, use_bias=False, is_training=phase_flag, name='Conv4b')
conv4_2 = conv3d(input=conv4_1_relu, output_chn=512, kernel_size=3, stride=1, use_bias=False, name='conv4b')
conv4_2_bn = tf.contrib.layers.batch_norm(conv4_2, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True, is_training=phase_flag, scope="conv4_2_batch_norm")
conv4_2_relu = tf.nn.relu(conv4_2_bn, name='conv4_2_relu')
pool4 = tf.layers.max_pooling3d(inputs=conv4_2_relu, pool_size=2, strides=2, name='pool4')
#
conv5_1 = conv_bn_relu(input=pool4, output_chn=512, kernel_size=3, stride=1, use_bias=False, is_training=phase_flag, name='conv5_1')
conv5_2 = conv_bn_relu(input=conv5_1, output_chn=512, kernel_size=3, stride=1, use_bias=False, is_training=phase_flag, name='conv5_2')
# up-sampling path
# compute up-sample path in gpu1
with tf.device("/gpu:1"):
deconv1_1 = deconv_bn_relu(input=conv5_2, output_chn=512, is_training=phase_flag, name='deconv1_1')
#
concat_1 = tf.concat([deconv1_1, conv4_2], axis=concat_dim, name='concat_1')
deconv1_2 = conv_bn_relu(input=concat_1, output_chn=256, kernel_size=3, stride=1, use_bias=False, is_training=phase_flag, name='deconv1_2')
deconv2_1 = deconv_bn_relu(input=deconv1_2, output_chn=256, is_training=phase_flag, name='deconv2_1')
#
concat_2 = tf.concat([deconv2_1, conv3_2], axis=concat_dim, name='concat_2')
deconv2_2 = conv_bn_relu(input=concat_2, output_chn=128, kernel_size=3, stride=1, use_bias=False, is_training=phase_flag, name='deconv2_2')
deconv3_1 = deconv_bn_relu(input=deconv2_2, output_chn=128, is_training=phase_flag, name='deconv3_1')
#
concat_3 = tf.concat([deconv3_1, conv2_1], axis=concat_dim, name='concat_3')
deconv3_2 = conv_bn_relu(input=concat_3, output_chn=64, kernel_size=3, stride=1, use_bias=False, is_training=phase_flag, name='deconv3_2')
deconv4_1 = deconv_bn_relu(input=deconv3_2, output_chn=64, is_training=phase_flag, name='deconv4_1')
#
concat_4 = tf.concat([deconv4_1, conv1_1], axis=concat_dim, name='concat_4')
deconv4_2 = conv_bn_relu(input=concat_4, output_chn=32, kernel_size=3, stride=1, use_bias=False, is_training=phase_flag, name='deconv4_2')
# predicted probability
pred_prob = conv3d(input=deconv4_2, output_chn=self.output_chn, kernel_size=1, stride=1, use_bias=True, name='pred_prob')
# ======================
# auxiliary prediction 0
aux0_conv = conv3d(input=deconv1_2, output_chn=self.output_chn, kernel_size=1, stride=1, use_bias=True, name='aux0_conv')
aux0_deconv_1 = Deconv3d(input=aux0_conv, output_chn=self.output_chn, name='aux0_deconv_1')
aux0_deconv_2 = Deconv3d(input=aux0_deconv_1, output_chn=self.output_chn, name='aux0_deconv_2')
aux0_prob = Deconv3d(input=aux0_deconv_2, output_chn=self.output_chn, name='aux0_prob')
# auxiliary prediction 1
aux1_conv = conv3d(input=deconv2_2, output_chn=self.output_chn, kernel_size=1, stride=1, use_bias=True, name='aux1_conv')
aux1_deconv_1 = Deconv3d(input=aux1_conv, output_chn=self.output_chn, name='aux1_deconv_1')
aux1_prob = Deconv3d(input=aux1_deconv_1, output_chn=self.output_chn, name='aux1_prob')
# auxiliary prediction 2
aux2_conv = conv3d(input=deconv3_2, output_chn=self.output_chn, kernel_size=1, stride=1, use_bias=True, name='aux2_conv')
aux2_prob = Deconv3d(input=aux2_conv, output_chn=self.output_chn, name='aux2_prob')
with tf.device("/cpu:0"):
# predicted labels
soft_prob = tf.nn.softmax(pred_prob, name='pred_soft')
pred_label = tf.argmax(soft_prob, axis=4, name='argmax')
return pred_prob, pred_label, aux0_prob, aux1_prob, aux2_prob
# network as discriminator
def discriminator(self, im_pair, reuse=False):
phase_flag = (self.phase == 'train')
with tf.variable_scope("discriminator") as scope:
if reuse:
tf.get_variable_scope().reuse_variables()
else:
assert tf.get_variable_scope().reuse == False
h0 = conv_bn_relu(input=im_pair, output_chn=32, kernel_size=3, stride=1, use_bias=True, is_training=phase_flag, name='d_h0_conv')
h1 = conv_bn_relu(input=h0, output_chn=64, kernel_size=3, stride=1, use_bias=True, is_training=phase_flag, name='d_h1_conv')
h2 = conv_bn_relu(input=h1, output_chn=64, kernel_size=3, stride=1, use_bias=True, is_training=phase_flag, name='d_h2_conv')
h3 = conv_bn_relu(input=h2, output_chn=64, kernel_size=3, stride=1, use_bias=True, is_training=phase_flag, name='d_h3_conv')
h4 = linear(tf.reshape(h3, [self.batch_size, -1]), 1, 'd_h3_lin')
return tf.nn.sigmoid(h4), h4
# train function
def train(self):
"""Train 3D U-net"""
d_optim = tf.train.AdamOptimizer(learning_rate=self.lr, beta1=self.beta1).minimize(self.d_loss, var_list=self.d_vars)
g_optim = tf.train.AdamOptimizer(learning_rate=self.lr, beta1=self.beta1).minimize(self.g_loss, var_list=self.g_vars)
# initialization
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
# =============
self.initialize_unet()
# =============
# save .log
self.log_writer = tf.summary.FileWriter("./logs", self.sess.graph)
counter = 1
if self.load_chkpoint(self.chkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
# load volume files
# modalities
# pair_list = glob('{}/*.nii.gz'.format(self.traindata_dir))
# pair_list.sort()
pair_list = []
for p in range(150):
img_path = os.path.join(self.traindata_dir, (str(p) + '.nii'))
gt_path = os.path.join(self.traindata_dir, (str(p) + '_seg.nii'))
pair_list.append(img_path)
pair_list.append(gt_path)
# a_img_clec, a_label_clec = load_data_pairs(a_pair_list, self.resize_r, self.rename_map)
img_clec, label_clec = load_data_pairs_padding(pair_list, self.resize_r, self.rename_map, pad_w=self.pad_w)
# temporary file to save loss
loss_log = open("loss.txt", "w")
all_loss = []
for epoch in np.arange(self.epoch):
start_time = time.time()
# train batch
batch_img, batch_label = get_batch_patches(img_clec, label_clec, self.inputI_size, self.batch_size, chn=1, flip_flag=True, rot_flag=True)
# ==================
# Update D network
self.sess.run([d_optim], feed_dict={self.real_I: batch_img, self.real_label: batch_label})
# Update G network
self.sess.run([g_optim], feed_dict={self.real_I: batch_img, self.real_label: batch_label})
# # Update G network
# self.sess.run([g_optim], feed_dict={self.real_I: batch_img, self.real_label: batch_label})
# # Update G network to make sure that d_loss does not go to zero
# self.sess.run([g_optim], feed_dict={self.real_I: batch_img, self.real_label: batch_label})
# ==================
# errors
errD_fake = self.d_loss_fake.eval({self.real_I: batch_img})
errD_real = self.d_loss_real.eval({self.real_I: batch_img, self.real_label: batch_label})
errG = self.g_loss.eval({self.real_I: batch_img, self.real_label: batch_label})
err_unet = self.g2g_loss.eval({self.real_I: batch_img, self.real_label: batch_label})
counter += 1
print("============")
print("Epoch: [%2d] time: %4.4f, d_loss: %.8f, g_loss: %.8f, unet_loss: %.8f" % (epoch, time.time() - start_time, errD_fake + errD_real, errG, err_unet))
all_loss.append([errD_fake + errD_real, errG, err_unet])
# record error
with open("cgan_err.txt", 'wb') as err_fid:
np.savetxt(err_fid, all_loss, fmt='%s')
if np.mod(counter, self.save_intval) == 0:
self.save_chkpoint(self.chkpoint_dir, self.model_name, counter)
# validation batch
batch_val_img, batch_val_label = get_batch_patches(img_clec, label_clec, self.inputI_size, self.batch_size, chn=1, flip_flag=True, rot_flag=True)
# current and validation loss
# cur_valid_loss = self.g2g_loss.eval({self.real_I: batch_val_img, self.real_label: batch_val_label})
cube_label = self.sess.run(self.fake_label, feed_dict={self.real_I: batch_val_img})
print np.unique(batch_label)
print np.unique(cube_label)
# dice value
dice_c = []
for c in range(self.output_chn):
ints = np.sum(((batch_val_label[0,:,:,:]==c)*1)*((cube_label[0,:,:,:]==c)*1))
union = np.sum(((batch_val_label[0,:,:,:]==c)*1) + ((cube_label[0,:,:,:]==c)*1)) + 0.0001
dice_c.append((2.0*ints)/union)
print dice_c
loss_log.close()
# test the model
def test(self):
"""Test 3D U-net"""
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
start_time = time.time()
if self.load_chkpoint(self.chkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
# get file list of testing dataset
test_list = glob('{}/*.nii.gz'.format(self.testdata_dir))
test_list.sort()
test_list = []
for p in range(56):
img_path = os.path.join(self.testdata_dir, (str(p) + '.nii'))
test_list.append(img_path)
# test
for k in range(0, len(test_list)):
print "processing No. %d volume..." % k
# load the volume
vol_file = nib.load(test_list[k])
ref_affine = vol_file.affine
# get volume data
vol_data = vol_file.get_data().copy()
resize_dim = (np.array(vol_data.shape) * self.resize_r).astype('int')
vol_data_resz = resize(vol_data, resize_dim, order=1, preserve_range=True)
# padding
vol_rz_pad = np.lib.pad(vol_data_resz, ((self.pad_w, self.pad_w), (self.pad_w, self.pad_w), (self.pad_w, self.pad_w)), 'constant',
constant_values=np.array(((0, 0), (0, 0), (0, 0))))
vol_pad_dim = vol_rz_pad.shape
# normalization
vol_rz_pad = vol_rz_pad.astype('float32')
vol_rz_pad = vol_rz_pad / 255.0
# decompose volume into list of cubes
cube_list = decompose_vol2cube(vol_rz_pad, self.batch_size, self.inputI_size, self.inputI_chn, self.ovlp_ita)
# predict on each cube
cube_label_list = []
for c in range(len(cube_list)):
cube2test = cube_list[c]
mean_temp = np.mean(cube2test)
dev_temp = np.std(cube2test)
cube2test_norm = (cube2test - mean_temp) / dev_temp
cube_label = self.sess.run(self.fake_label, feed_dict={self.real_I: cube2test_norm})
cube_label_list.append(cube_label)
# print np.unique(cube_label)
# compose cubes into a volume
composed_orig = compose_label_cube2vol(cube_label_list, vol_pad_dim, self.inputI_size, self.ovlp_ita, self.output_chn)
composed_label = np.zeros(composed_orig.shape, dtype='int16')
# rename label
for i in range(len(self.rename_map)):
composed_label[composed_orig == i] = self.rename_map[i]
composed_label = composed_label.astype('int16')
# remove padding
composed_label = composed_label[self.pad_w:self.pad_w+resize_dim[0], self.pad_w:self.pad_w+resize_dim[1], self.pad_w:self.pad_w+resize_dim[2]]
print np.unique(composed_label)
# for s in range(composed_label.shape[2]):
# cv2.imshow('volume_seg', np.concatenate(((vol_data_resz[:, :, s]).astype('uint8'), (composed_label[:, :, s]/4).astype('uint8')), axis=1))
# cv2.waitKey(30)
# save predicted label
composed_label_resz = resize(composed_label, vol_data.shape, order=0, preserve_range=True)
composed_label_resz = composed_label_resz.astype('int16')
labeling_path = os.path.join(self.labeling_dir, ('test_' + str(k) + '.nii.gz'))
labeling_vol = nib.Nifti1Image(composed_label_resz, ref_affine)
nib.save(labeling_vol, labeling_path)
# test function for cross validation
def test4crsv(self):
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
start_time = time.time()
if self.load_chkpoint(self.chkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
# get file list of testing dataset
test_list = glob('{}/*.nii.gz'.format(self.testdata_dir))
test_list.sort()
# all dice
all_dice = np.zeros([int(len(test_list)/2), 8])
# test
for k in range(2, len(test_list), 2):
# load the volume
vol_file = nib.load(test_list[k])
ref_affine = vol_file.affine
# get volume data
vol_data = vol_file.get_data().copy()
resize_dim = (np.array(vol_data.shape) * self.resize_r).astype('int')
vol_data_resz = resize(vol_data, resize_dim, order=1, preserve_range=True)
# normalization
vol_data_resz = vol_data_resz.astype('float32')
vol_data_resz = vol_data_resz / 255.0
# padding
vol_rz_pad = np.lib.pad(vol_data_resz, ((self.pad_w, self.pad_w), (self.pad_w, self.pad_w), (self.pad_w, self.pad_w)), 'constant',
constant_values=np.array(((0, 0), (0, 0), (0, 0))))
vol_pad_dim = vol_rz_pad.shape
# decompose volume into list of cubes
cube_list = decompose_vol2cube(vol_rz_pad, self.batch_size, self.inputI_size, self.inputI_chn, self.ovlp_ita)
# predict on each cube
cube_label_list = []
for c in range(len(cube_list)):
cube2test = cube_list[c]
mean_temp = np.mean(cube2test)
dev_temp = np.std(cube2test)
cube2test_norm = (cube2test - mean_temp) / dev_temp
cube_label = self.sess.run(self.fake_label, feed_dict={self.real_I: cube2test_norm})
cube_label_list.append(cube_label)
# print np.unique(cube_label)
# compose cubes into a volume
composed_orig = compose_label_cube2vol(cube_label_list, vol_pad_dim, self.inputI_size, self.ovlp_ita, self.output_chn)
composed_label = np.zeros(composed_orig.shape, dtype='int16')
# rename label
for i in range(len(self.rename_map)):
composed_label[composed_orig == i] = self.rename_map[i]
composed_label = composed_label.astype('int16')
# remove padding
composed_label = composed_label[self.pad_w:self.pad_w+resize_dim[0], self.pad_w:self.pad_w+resize_dim[1], self.pad_w:self.pad_w+resize_dim[2]]
print np.unique(composed_label)
for s in range(composed_label.shape[2]):
cv2.imshow('volume_seg', np.concatenate(((vol_data_resz[:, :, s]*255.0).astype('uint8'), (composed_label[:, :, s]/4).astype('uint8')), axis=1))
cv2.waitKey(30)
# save predicted label
composed_label_resz = resize(composed_label, vol_data.shape, order=0, preserve_range=True)
composed_label_resz = composed_label_resz.astype('int16')
labeling_path = os.path.join(self.labeling_dir, ('test_' + str(k) + '.nii.gz'))
labeling_vol = nib.Nifti1Image(composed_label_resz, ref_affine)
nib.save(labeling_vol, labeling_path)
# evaluation
gt_file = nib.load(test_list[k + 1])
gt_label = gt_file.get_data().copy()
k_dice_c = seg_eval_metric(composed_label_resz, gt_label)
print k_dice_c
all_dice[int(k/2), :] = np.asarray(k_dice_c)
mean_dice = np.mean(all_dice, axis=0)
print "average dice: "
print mean_dice
# save checkpoint file
def save_chkpoint(self, checkpoint_dir, model_name, step):
model_dir = "%s_%s" % (self.batch_size, self.outputI_size)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.saver.save(self.sess, os.path.join(checkpoint_dir, model_name), global_step=step)
# load checkpoint file
def load_chkpoint(self, checkpoint_dir):
print(" [*] Reading checkpoint...")
model_dir = "%s_%s" % (self.batch_size, self.outputI_size)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
return True
else:
return False
# load pre-trained unet
def initialize_unet(self):
checkpoint_dir = '/media/xinyang/echo2/tmi17_pkg/code/GAN/FCN_cGAN_3D_full/3d_unet/outcome/model/pre-train_unet/1_80'
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
self.saver_unet.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))