def run_net(self, no_averages):
self.alpha_coeff = 1
'''read path of the images for train, test, and validation'''
_rd = _read_data(self.data_path_AMUC, self.data_path_LUMC)
train_data, validation_data, test_data = _rd.read_data_path(
average_no=no_averages)
# ======================================
bunch_of_images_no = 1
_image_class_vl = image_class(validation_data,
bunch_of_images_no=bunch_of_images_no,
is_training=0,
inp_size=self.asl_size,
out_size=self.pet_size)
_patch_extractor_thread_vl = _patch_extractor_thread._patch_extractor_thread(
_image_class=_image_class_vl,
img_no=bunch_of_images_no,
mutex=settings.mutex,
is_training=0)
_fill_thread_vl = fill_thread.fill_thread(
validation_data,
_image_class_vl,
mutex=settings.mutex,
is_training=0,
patch_extractor=_patch_extractor_thread_vl,
)
_read_thread_vl = read_thread.read_thread(
_fill_thread_vl,
mutex=settings.mutex,
validation_sample_no=self.validation_samples,
is_training=0)
_fill_thread_vl.start()
_patch_extractor_thread_vl.start()
_read_thread_vl.start()
# ======================================
bunch_of_images_no = 14
_image_class_tr = image_class(train_data,
bunch_of_images_no=bunch_of_images_no,
is_training=1,
inp_size=self.asl_size,
out_size=self.pet_size)
_patch_extractor_thread_tr = _patch_extractor_thread._patch_extractor_thread(
_image_class=_image_class_tr,
img_no=bunch_of_images_no,
mutex=settings.mutex,
is_training=1,
)
_fill_thread = fill_thread.fill_thread(
train_data,
_image_class_tr,
mutex=settings.mutex,
is_training=1,
patch_extractor=_patch_extractor_thread_tr,
)
_read_thread = read_thread.read_thread(_fill_thread,
mutex=settings.mutex,
is_training=1)
_fill_thread.start()
_patch_extractor_thread_tr.start()
_read_thread.start()
# ======================================
# asl_plchld= tf.placeholder(tf.float32, shape=[None, None, None, 1])
# t1_plchld= tf.placeholder(tf.float32, shape=[None, None, None, 1])
# pet_plchld= tf.placeholder(tf.float32, shape=[None, None, None, 1])
asl_plchld = tf.placeholder(
tf.float32, shape=[self.batch_no, self.asl_size, self.asl_size, 1])
t1_plchld = tf.placeholder(
tf.float32, shape=[self.batch_no, self.asl_size, self.asl_size, 1])
pet_plchld = tf.placeholder(
tf.float32, shape=[self.batch_no, self.pet_size, self.pet_size, 1])
asl_out_plchld = tf.placeholder(
tf.float32, shape=[self.batch_no, self.pet_size, self.pet_size, 1])
residual_attention_map = tf.placeholder(
tf.float32, shape=[self.batch_no, self.asl_size, self.asl_size, 1])
ave_loss_vali = tf.placeholder(tf.float32, name='ave_loss_vali')
# True: train in a multi-task fashion, False: train in a single-task fashion
hybrid_training_flag = tf.placeholder(tf.bool,
name='hybrid_training_flag')
is_training = tf.placeholder(tf.bool, name='is_training')
is_training_bn = tf.placeholder(tf.bool, name='is_training_bn')
# cnn_net = unet() # create object
# y,augmented_data = cnn_net.unet(t1=t1_plchld, asl=asl_plchld, pet=pet_plchld, is_training_bn=is_training_bn)
msdensnet = multi_stage_densenet()
asl_y, pet_y, att_map = msdensnet.multi_stage_densenet(
asl_img=asl_plchld,
t1_img=t1_plchld,
pet_img=pet_plchld,
hybrid_training_flag=hybrid_training_flag,
input_dim=self.asl_size,
is_training=is_training,
config=self.config,
residual_attention_map=residual_attention_map)
show_img = asl_plchld[:, :, :, 0, np.newaxis]
tf.summary.image('00: input_asl', show_img, 3)
show_img = t1_plchld[:, :, :, 0, np.newaxis]
tf.summary.image('01: input_t1', show_img, 3)
show_img = pet_plchld[:, :, :, 0, np.newaxis]
tf.summary.image('02: target_pet', show_img, 3)
show_img = asl_out_plchld[:, :, :, 0, np.newaxis]
tf.summary.image('03: target_asl', show_img, 3)
#
show_img = asl_y[:, :, :, 0, np.newaxis]
tf.summary.image('04: output_asl', show_img, 3)
show_img = pet_y[:, :, :, 0, np.newaxis]
tf.summary.image('05: output_pet', show_img, 3)
# -----------------
# show_img = loss_upsampling11[:, :, :, 0, np.newaxis]
# tf.summary.image('04: loss_upsampling11', show_img, 3)
# #
# show_img = loss_upsampling22[:, :, :, 0, np.newaxis]
# tf.summary.image('05: loss_upsampling22', show_img, 3)
print('*****************************************')
print('*****************************************')
print('*****************************************')
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# devices = sess.list_devices()
# print(devices)
# print(device_lib.list_local_devices())
print('*****************************************')
print('*****************************************')
print('*****************************************')
train_writer = tf.summary.FileWriter(self.LOGDIR + '/train',
graph=tf.get_default_graph())
validation_writer = tf.summary.FileWriter(self.LOGDIR + '/validation',
graph=sess.graph)
try:
os.mkdir(self.LOGDIR + 'code/')
copyfile('./run_hybrid_multitask_net_hr.py',
self.LOGDIR + 'code/run_hybrid_multitask_net_hr.py')
copyfile('./submit_job_hybrid_multitask_hr.py',
self.LOGDIR + 'code/submit_job_hybrid_multitask_hr.pys')
shutil.copytree('./functions/', self.LOGDIR + 'code/functions/')
except:
a = 1
# validation_writer.flush()
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=1000)
# train_writer.close()
# validation_writer.close()
loadModel = 0
# self.loss = ssim_loss()
alpha = .84
with tf.name_scope('cost'):
ssim_asl = tf.reduce_mean(
1 - SSIM(x1=asl_out_plchld, x2=asl_y, max_val=34.0)[0])
loss_asl = alpha * ssim_asl + (1 - alpha) * tf.reduce_mean(
huber(labels=asl_out_plchld, logit=asl_y))
ssim_pet = tf.reduce_mean(
1 - SSIM(x1=pet_plchld, x2=pet_y, max_val=2.1)[0])
loss_pet = alpha * ssim_pet + (1 - alpha) * tf.reduce_mean(
huber(labels=pet_plchld, logit=pet_y))
# loss_pet = tf.cond(hybrid_training_flag,
# lambda:alpha * ssim_pet + (1 - alpha) * tf.reduce_mean(huber(labels=pet_plchld, logit=pet_y)),
# lambda:tf.stop_gradient(alpha * ssim_pet + (1 - alpha) * tf.reduce_mean(huber(labels=pet_plchld, logit=pet_y))))
# cost = tf.cond(hybrid_training_flag, lambda: loss_asl + loss_pet,
# lambda: loss_asl )
# cost = tf.cond(hybrid_training_flag, lambda: ssim_asl + ssim_pet,
# lambda: ssim_asl )
cost_withpet = tf.reduce_mean(loss_asl + loss_pet)
cost_withoutpet = loss_asl
# hybrid: 0 without pet, 1 with pet
# with tf.name_scope('cost'):
# ssim_asl = tf.reduce_mean(1 - SSIM(x1=asl_out_plchld, x2=asl_y, max_val=34.0)[0])
# # loss_asl = alpha * ssim_asl + (1 - alpha) * tf.reduce_mean(huber(labels=asl_out_plchld, logit=asl_y))
# loss_asl =ssim_asl
#
# ssim_pet = tf.reduce_mean(1 - SSIM(x1=pet_plchld, x2=pet_y, max_val=2.1)[0])
# # loss_pet = alpha * ssim_pet + (1 - alpha) * tf.reduce_mean(huber(labels=pet_plchld, logit=pet_y))
# loss_pet =ssim_pet
#
# # cost = tf.cond(hybrid_training_flag, lambda: loss_asl+tf.stop_gradient(loss_pet), lambda: loss_asl+loss_pet)
# # cost = tf.cond(hybrid_training_flag,
# # lambda: loss_asl+0*tf.stop_gradient(loss_pet), #true of 1 without pet
# # lambda: loss_asl+loss_pet) #false or 0 with pet
#
# cost = loss_pet
tf.summary.scalar("cost_withoutpet", cost_withoutpet)
tf.summary.scalar("cost_withpet", cost_withpet)
# tf.summary.scalar("denominator", denominator)
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(extra_update_ops):
optimizer_withpet = tf.train.AdamOptimizer(
learning_rate=self.learning_rate, ).minimize(cost_withpet)
optimizer_withoutpet = tf.train.AdamOptimizer(
learning_rate=self.learning_rate, ).minimize(cost_withoutpet)
with tf.name_scope('validation'):
average_validation_loss = ave_loss_vali
tf.summary.scalar("average_validation_loss", average_validation_loss)
sess.run(tf.global_variables_initializer())
logging.debug('total number of variables %s' % (np.sum([
np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()
])))
summ = tf.summary.merge_all()
point = 0
itr1 = 0
if loadModel:
chckpnt_dir = ''
ckpt = tf.train.get_checkpoint_state(chckpnt_dir)
saver.restore(sess, ckpt.model_checkpoint_path)
point = np.int16(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
itr1 = point
# with tf.Session() as sess:
print("Number of trainable parameters: %d" %
self.count_number_trainable_params())
# patch_radius = 49
'''loop for epochs'''
for itr in range(self.total_iteration):
while self.no_sample_per_each_itr * int(
point / self.no_sample_per_each_itr) < self.sample_no:
print("epoch #: %d" % (settings.epochs_no))
startTime = time.time()
step = 0
self.beta_coeff = 1 + 1 * np.exp(-point / 2000)
# =============validation================
if itr1 % self.display_validation_step == 0:
'''Validation: '''
loss_validation = 0
acc_validation = 0
validation_step = 0
dsc_validation = 0
while (validation_step * self.batch_no_validation <
settings.validation_totalimg_patch):
[
validation_asl_slices, validation_pet_slices,
validation_t1_slices
] = _image_class_vl.return_patches_validation(
validation_step * self.batch_no_validation,
(validation_step + 1) * self.batch_no_validation)
if (len(validation_asl_slices) <
self.batch_no_validation) | (
len(validation_pet_slices) <
self.batch_no_validation) | (
len(validation_t1_slices) <
self.batch_no_validation):
_read_thread_vl.resume()
time.sleep(0.5)
# print('sleep 3 validation')
continue
# if len(validation_pet_slices):
# hybrid_training_f = True
# else:
# hybrid_training_f = False
tic = time.time()
[loss_vali] = sess.run(
[cost_withpet],
feed_dict={
asl_plchld:
validation_asl_slices,
t1_plchld:
validation_t1_slices,
pet_plchld:
validation_pet_slices,
asl_out_plchld:
validation_asl_slices[:,
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size /
2) +
int(self.pet_size / 2),
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size /
2) +
int(self.pet_size /
2), :],
is_training:
False,
ave_loss_vali:
-1,
is_training_bn:
False,
hybrid_training_flag:
False,
residual_attention_map: (np.ones([
self.batch_no, self.asl_size,
self.asl_size, 1
]))
})
elapsed = time.time() - tic
loss_validation += loss_vali
validation_step += 1
if np.isnan(dsc_validation) or np.isnan(
loss_validation) or np.isnan(acc_validation):
print('nan problem')
process = psutil.Process(os.getpid())
print(
'%d - > %d: elapsed_time:%d loss_validation: %f, memory_percent: %4s'
% (
validation_step,
validation_step * self.batch_no_validation,
elapsed,
loss_vali,
str(process.memory_percent()),
))
# end while
settings.queue_isready_vl = False
acc_validation = acc_validation / (validation_step)
loss_validation = loss_validation / (validation_step)
dsc_validation = dsc_validation / (validation_step)
if np.isnan(dsc_validation) or np.isnan(
loss_validation) or np.isnan(acc_validation):
print('nan problem')
_fill_thread_vl.kill_thread()
print(
'******Validation, step: %d , accuracy: %.4f, loss: %f*******'
% (itr1, acc_validation, loss_validation))
[sum_validation] = sess.run(
[summ],
feed_dict={
asl_plchld:
validation_asl_slices,
t1_plchld:
validation_t1_slices,
pet_plchld:
validation_pet_slices,
asl_out_plchld:
validation_asl_slices[:,
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size / 2) +
int(self.pet_size / 2),
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size / 2) +
int(self.pet_size / 2), :],
is_training:
False,
ave_loss_vali:
loss_validation,
is_training_bn:
False,
hybrid_training_flag:
False,
residual_attention_map: (np.ones([
self.batch_no, self.asl_size, self.asl_size, 1
]))
})
validation_writer.add_summary(sum_validation, point)
validation_writer.flush()
print('end of validation---------%d' % (point))
# end if
'''loop for training batches'''
db = ''
patch_step = 0
while (step * self.batch_no < self.no_sample_per_each_itr):
if patch_step < 5: # hybrid: 0 without pet, 1 with pet
hybrid_training_f = True
db = 'AMUC'
elif patch_step < 10:
hybrid_training_f = False
db = 'LUMC'
else:
patch_step = 0
# hybrid_training_f = True
patch_step = patch_step + 1
[train_asl_slices, train_pet_slices,
train_t1_slices] = _image_class_tr.return_patches(
self.batch_no, hybrid_training_f)
# if not hybrid_training_f:
# continue
if (len(train_asl_slices) < self.batch_no) | (len(train_pet_slices) < self.batch_no) \
| (len(train_t1_slices) < self.batch_no):
# |(len(train_t1_slices)<self.batch_no):
time.sleep(0.5)
_read_thread.resume()
continue
tic = time.time()
# hybrid_training_f=True
if hybrid_training_f: #AMUC
[loss_train1, opt, att_map1] = sess.run(
[cost_withpet, optimizer_withpet, att_map],
feed_dict={
asl_plchld:
train_asl_slices,
t1_plchld:
train_t1_slices,
pet_plchld:
train_pet_slices,
asl_out_plchld:
train_asl_slices[:,
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size / 2) +
int(self.pet_size / 2),
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size / 2) +
int(self.pet_size / 2), :],
is_training:
True,
ave_loss_vali:
-1,
is_training_bn:
True,
hybrid_training_flag:
hybrid_training_f,
residual_attention_map: (np.ones([
self.batch_no, self.asl_size,
self.asl_size, 1
]))
})
#----
att_map_padded = np.zeros(
[self.batch_no, self.asl_size, self.asl_size, 1])
att_map_padded[:,
int((self.asl_size - self.pet_size) /
2 - 1):int((self.asl_size -
self.pet_size) / 2 +
self.pet_size - 1),
int((self.asl_size - self.pet_size) /
2 - 1):int((self.asl_size -
self.pet_size) / 2 +
self.pet_size -
1), :] = att_map1
#----
[loss_train1, opt] = sess.run(
[cost_withpet, optimizer_withpet],
feed_dict={
asl_plchld:
train_asl_slices,
t1_plchld:
train_t1_slices,
pet_plchld:
train_pet_slices,
asl_out_plchld:
train_asl_slices[:,
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size / 2) +
int(self.pet_size / 2),
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size / 2) +
int(self.pet_size / 2), :],
is_training:
True,
ave_loss_vali:
-1,
is_training_bn:
True,
hybrid_training_flag:
hybrid_training_f,
residual_attention_map:
att_map_padded
})
else: #LUMC
[loss_train1, opt, att_map1] = sess.run(
[cost_withoutpet, optimizer_withoutpet, att_map],
feed_dict={
asl_plchld:
train_asl_slices,
t1_plchld:
train_t1_slices,
pet_plchld:
train_pet_slices,
asl_out_plchld:
train_asl_slices[:,
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size / 2) +
int(self.pet_size / 2),
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size / 2) +
int(self.pet_size / 2), :],
is_training:
True,
ave_loss_vali:
-1,
is_training_bn:
True,
hybrid_training_flag:
hybrid_training_f,
residual_attention_map: (np.ones([
self.batch_no, self.asl_size,
self.asl_size, 1
]))
})
# ----
att_map_padded = np.zeros(
[self.batch_no, self.asl_size, self.asl_size, 1])
att_map_padded[:,
int((self.asl_size - self.pet_size) /
2 - 1):int((self.asl_size -
self.pet_size) / 2 +
self.pet_size - 1),
int((self.asl_size - self.pet_size) /
2 - 1):int((self.asl_size -
self.pet_size) / 2 +
self.pet_size -
1), :] = att_map1
# ----
[loss_train1, opt] = sess.run(
[
cost_withoutpet,
optimizer_withoutpet,
],
feed_dict={
asl_plchld:
train_asl_slices,
t1_plchld:
train_t1_slices,
pet_plchld:
train_pet_slices,
asl_out_plchld:
train_asl_slices[:,
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size / 2) +
int(self.pet_size / 2),
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size / 2) +
int(self.pet_size / 2), :],
is_training:
True,
ave_loss_vali:
-1,
is_training_bn:
True,
hybrid_training_flag:
hybrid_training_f,
residual_attention_map:
att_map_padded
})
elapsed = time.time() - tic
[sum_train] = sess.run(
[summ],
feed_dict={
asl_plchld:
train_asl_slices,
t1_plchld:
train_t1_slices,
pet_plchld:
train_pet_slices,
asl_out_plchld:
train_asl_slices[:,
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size / 2) +
int(self.pet_size / 2),
int(self.asl_size / 2) -
int(self.pet_size / 2) -
1:int(self.asl_size / 2) +
int(self.pet_size / 2), :],
is_training:
False,
ave_loss_vali:
loss_train1,
is_training_bn:
False,
hybrid_training_flag:
hybrid_training_f,
residual_attention_map: (np.ones([
self.batch_no, self.asl_size, self.asl_size, 1
]))
})
train_writer.add_summary(sum_train, point)
train_writer.flush()
step = step + 1
process = psutil.Process(os.getpid())
print(
'point: %d, elapsed_time:%d step*self.batch_no:%f , LR: %.15f, loss_train1:%f,memory_percent: %4s, %s'
% (int((point)), elapsed, step * self.batch_no,
self.learning_rate, loss_train1,
str(process.memory_percent()), db))
point = int(
(point)
) # (self.no_sample_per_each_itr/self.batch_no)*itr1+step
if point % 100 == 0:
'''saveing model inter epoch'''
chckpnt_path = os.path.join(
self.chckpnt_dir,
('densenet_unet_inter_epoch%d_point%d.ckpt' %
(itr, point)))
saver.save(sess, chckpnt_path, global_step=point)
itr1 = itr1 + 1
point = point + 1
endTime = time.time()
# ==============end of epoch:
'''saveing model after each epoch'''
chckpnt_path = os.path.join(self.chckpnt_dir, 'densenet_unet.ckpt')
saver.save(sess, chckpnt_path, global_step=itr)
print("End of epoch----> %d, elapsed time: %d" %
(settings.epochs_no, endTime - startTime))
def test_all_nets(out_dir, Log, which_data):
data_path_AMUC = "/exports/lkeb-hpc/syousefi/Data/ASL2PET_high_res/AMUC_high_res/"
data_path_LUMC = "/exports/lkeb-hpc/syousefi/Data/ASL2PET_high_res/LUMC_high_res/"
_rd = _read_data(data_path_AMUC,data_path_LUMC)
train_data, validation_data, test_data = _rd.read_data_path(0)
if which_data == 1:
data = validation_data
elif which_data == 2:
data = test_data
elif which_data == 3:
data = train_data
asl_plchld = tf.placeholder(tf.float32, shape=[None, asl_size, asl_size, 1])
t1_plchld = tf.placeholder(tf.float32, shape=[None, asl_size, asl_size, 1])
pet_plchld = tf.placeholder(tf.float32, shape=[None, pet_size, pet_size, 1])
asl_out_plchld = tf.placeholder(tf.float32, shape=[None, pet_size, pet_size, 1])
hybrid_training_flag = tf.placeholder(tf.bool, name='hybrid_training_flag')
ave_loss_vali = tf.placeholder(tf.float32)
is_training = tf.placeholder(tf.bool, name='is_training')
is_training_bn = tf.placeholder(tf.bool, name='is_training_bn')
msdensnet = multi_stage_densenet()
asl_y1,asl_y2, pet_y = msdensnet.multi_stage_densenet(asl_img=asl_plchld,
t1_img=t1_plchld,
pet_img=pet_plchld,
hybrid_training_flag=hybrid_training_flag,
input_dim=asl_size,
is_training=is_training,
config=config,
)
alpha = .84
with tf.name_scope('cost'):
ssim_asl1 = tf.reduce_mean(1 - SSIM(x1=asl_out_plchld, x2=asl_y1, max_val=34.0)[0])
loss_asl1 = alpha * ssim_asl1 + (1 - alpha) * tf.reduce_mean(huber(labels=asl_out_plchld, logit=asl_y1))
ssim_asl = tf.reduce_mean(1 - SSIM(x1=asl_out_plchld, x2=asl_y2, max_val=34.0)[0])
loss_asl = alpha * ssim_asl + (1 - alpha) * tf.reduce_mean(huber(labels=asl_out_plchld, logit=asl_y2))
ssim_pet = tf.reduce_mean(1 - SSIM(x1=pet_plchld, x2=pet_y, max_val=2.1)[0])
loss_pet = alpha * ssim_pet + (1 - alpha) * tf.reduce_mean(huber(labels=pet_plchld, logit=pet_y))
cost_withpet = tf.reduce_mean(loss_asl + loss_asl1 + loss_pet)
cost_withoutpet = loss_asl + loss_asl1
sess = tf.Session()
saver = tf.train.Saver()
parent_path = '/exports/lkeb-hpc/syousefi/Code/'
chckpnt_dir = parent_path + Log + 'unet_checkpoints/'
ckpt = tf.train.get_checkpoint_state(chckpnt_dir)
saver.restore(sess, ckpt.model_checkpoint_path)
_meas = _measure()
copyfile('./test_semisupervised_multitast_hr_residual_asl.py',
parent_path + Log + out_dir + 'test_semisupervised_multitast_hr_residual_asl.py')
_image_class = image_class(data,
bunch_of_images_no=1,
is_training=0,
inp_size=asl_size,
out_size=pet_size)
list_ssim = []
list_name = []
list_ssim_NC = []
list_ssim_HC = []
for scan in range(len(data)):
ss = str(data[scan]['asl']).split("/")
imm = _image_class.read_image(data[scan])
try:
os.mkdir(parent_path + Log + out_dir + ss[-3])
os.mkdir(parent_path + Log + out_dir + ss[-3] + '/' + ss[-1].split(".")[0].split("ASL_")[1])
except:
a = 1
for img_indx in range(np.shape(imm[3])[0]):
print('img_indx:%s' % (img_indx))
name = ss[-3] + '_' + ss[-2] + '_' + str(img_indx)
# name = (ss[10] + '_' + ss[11] + '_' + ss[12].split('%')[0]).split('_CT')[0]
tic = time.time()
t1 = imm[3][img_indx,
int(imm[3].shape[-1]/2) - int(asl_size / 2) - 1:int(imm[3].shape[-1]/2) + int(asl_size / 2),
int(imm[3].shape[-1] / 2) - int(asl_size / 2) - 1:int(imm[3].shape[-1]/2) + int(asl_size / 2)]
t1 = t1[np.newaxis, ..., np.newaxis]
asl = imm[4][np.newaxis, img_indx,
int(imm[3].shape[-1] / 2) - int(asl_size / 2) - 1:int(imm[3].shape[-1] / 2) + int(asl_size / 2),
int(imm[3].shape[-1] / 2) - int(asl_size / 2) - 1:int(imm[3].shape[-1] / 2)+ int(asl_size / 2), np.newaxis]
pet = imm[5][np.newaxis, img_indx,
int(imm[3].shape[-1] / 2)- int(pet_size / 2) - 1:int(imm[3].shape[-1] / 2) + int(pet_size / 2),
int(imm[3].shape[-1] / 2)- int(pet_size / 2) - 1:int(imm[3].shape[-1] / 2) + int(pet_size / 2), np.newaxis]
[loss,pet_out,asl_out1,asl_out2] = sess.run([ssim_pet,pet_y,asl_y1,asl_y2],
feed_dict={asl_plchld: asl,
t1_plchld: t1,
pet_plchld: pet,
asl_out_plchld: asl[:,
int(asl_size / 2) - int(pet_size / 2) - 1:
int(asl_size / 2) + int(pet_size / 2),
int(asl_size / 2) - int(pet_size / 2) - 1:
int(asl_size / 2) + int(pet_size / 2), :],
is_training: False,
ave_loss_vali: -1,
is_training_bn: False,
hybrid_training_flag: False
})
ssim = 1 - loss
list_ssim.append(ssim)
str_nm = (ss[-3] + '_' + ss[-1].split(".")[0].split("ASL_")[1] + '_t1_' + name)
if 'HN' in str_nm:
list_ssim_NC.append(ssim)
elif 'HY' in str_nm:
list_ssim_HC.append(ssim)
list_name.append(ss[-3] + '_' + ss[-1].split(".")[0].split("ASL_")[1] + '_t1_' + name)
print(list_name[img_indx],': ',ssim)
matplotlib.image.imsave(parent_path + Log + out_dir + ss[-3] + '/' + ss[-1].split(".")[0].split("ASL_")[
1] + '/t1_' + name + '.png', np.squeeze(t1), cmap='gray')
matplotlib.image.imsave(parent_path + Log + out_dir + ss[-3] + '/' + ss[-1].split(".")[0].split("ASL_")[
1] + '/asl_' + name + '_' + '.png', np.squeeze(asl), cmap='gray')
matplotlib.image.imsave(parent_path + Log + out_dir + ss[-3] + '/' + ss[-1].split(".")[0].split("ASL_")[
1] + '/pet_' + name + '_' + '.png', np.squeeze(pet), cmap='gray')
matplotlib.image.imsave(parent_path + Log + out_dir + ss[-3] + '/' + ss[-1].split(".")[0].split("ASL_")[
1] + '/res_asl' + name + '_' + str(ssim) + '.png', np.squeeze(asl_out2), cmap='gray')
matplotlib.image.imsave(parent_path + Log + out_dir + ss[-3] + '/' + ss[-1].split(".")[0].split("ASL_")[
1] + '/res_pet' + name + '_' + str(ssim) + '.png', np.squeeze(pet_out), cmap='gray')
elapsed = time.time() - tic
df = pd.DataFrame(list_ssim,
columns=pd.Index(['ssim'],
name='Genus')).round(2)
a = {'HC': list_ssim_HC, 'NC': list_ssim_NC}
df2 = pd.DataFrame.from_dict(a, orient='index')
df2.transpose()
# Create a Pandas Excel writer using XlsxWriter as the engine.
writer = pd.ExcelWriter(parent_path + Log + out_dir + '/all_ssim.xlsx',
engine='xlsxwriter')
writer2 = pd.ExcelWriter(parent_path + Log + out_dir + '/all_ssim.xlsx',
engine='xlsxwriter')
# Convert the dataframe to an XlsxWriter Excel object.
df.to_excel(writer, sheet_name='Sheet1')
df2.to_excel(writer2, sheet_name='Sheet2')
# Close the Pandas Excel writer and output the Excel file.
writer.save()
writer2.save()
print(parent_path + Log + out_dir + '/all_ssim.xlsx')