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)
fold = 2
train_data, validation_data, test_data = _rd.read_data_path(average_no=0,
fold=fold)
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)
residual_attention_map = tf.placeholder(
tf.float32, shape=[None, asl_size, asl_size, 1])
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_y, pet_y, new_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=asl_size,
is_training=is_training,
config=config,
residual_attention_map=residual_attention_map)
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))
cost_withpet = tf.reduce_mean(loss_asl + loss_pet)
cost_withoutpet = loss_asl
mse = MSE(x1=pet_plchld, x2=pet_y)
psnr = PSNR(x1=pet_plchld, x2=pet_y)
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_rest_residual_skip_attention.py',
parent_path + Log + out_dir +
'test_semisupervised_multitast_hr_rest_residual_skip_attention.py')
_image_class = image_class(data,
bunch_of_images_no=1,
is_training=0,
inp_size=asl_size,
out_size=pet_size)
list_ssim_pet = []
list_ssim_asl = []
list_name = []
list_ssim_NC = []
list_mse_NC = []
list_psnr_NC = []
list_ssim_HC = []
list_mse_HC = []
list_psnr_HC = []
for scan in range(len(data)):
ss = str(data[scan]['asl']).split("/")
imm = _image_class.read_image(data[scan])
try:
# print(parent_path + Log + out_dir + ss[-3])
os.mkdir(parent_path + Log + out_dir + ss[-3])
except:
a = 1
# print(parent_path + Log + out_dir + ss[-3] + '/' + ss[-1].split(".")[0].split("ASL_")[1])
try:
os.mkdir(parent_path + Log + out_dir + ss[-3] + '/' +
ss[-1].split(".")[0].split("ASL_")[1])
except:
try:
os.mkdir(parent_path + Log + out_dir + ss[-3] + '/' +
ss[-1].split(".")[0])
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]
if np.size(imm[5]) > 1:
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]
hybrid_training_f = True
else:
hybrid_training_f = False
pet = np.reshape([None] * pet_size * pet_size,
[pet_size, pet_size])
pet = pet[..., np.newaxis]
pet = pet[np.newaxis, ...]
# if hybrid_training_f:
[loss, psnr1, mse1, pet_out, asl_out] = sess.run(
[ssim_pet, psnr, mse, pet_y, asl_y],
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,
residual_attention_map:
(np.ones([1, asl_size, asl_size, 1]))
})
# if hybrid_training_f:
ssim = 1 - loss
list_ssim_pet.append(ssim)
# else:
# [loss,asl_out ] = sess.run([ssim_asl,asl_y ],
# 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:True })
# ssim = 1 - loss
# list_ssim_asl.append(ssim)
try:
str_nm = (ss[-3] + '_' +
ss[-1].split(".")[0].split("ASL_")[1] + '_t1_' +
name)
except:
str_nm = (ss[-3] + '_' + ss[-1].split(".")[0] + '_t1_' + name)
if 'HN' in str_nm:
list_ssim_NC.append(ssim)
list_psnr_NC.append(psnr1)
list_mse_NC.append(mse1)
elif 'HY' in str_nm:
list_ssim_HC.append(ssim)
list_psnr_HC.append(psnr1)
list_mse_HC.append(mse1)
try:
list_name.append(ss[-3] + '_' +
ss[-1].split(".")[0].split("ASL_")[1] +
'_t1_' + name)
nm_fig = parent_path + Log + out_dir + ss[-3] + '/' + ss[
-1].split(".")[0].split("ASL_")[1]
except:
list_name.append(ss[-3] + '_' + ss[-1].split(".")[0] + '_t1_' +
name)
nm_fig = parent_path + Log + out_dir + ss[-3] + '/' + ss[
-1].split(".")[0]
print(list_name[img_indx], ': ', ssim, ',PSNR: ', psnr1, ',MSE: ',
mse1)
sitk.WriteImage(sitk.GetImageFromArray(np.squeeze(t1)),
nm_fig + '/t1_' + name + '.mha')
sitk.WriteImage(sitk.GetImageFromArray(np.squeeze(asl)),
nm_fig + '/asl_' + name + '_' + '.mha')
if hybrid_training_f:
sitk.WriteImage(sitk.GetImageFromArray(np.squeeze(pet)),
nm_fig + '/pet_' + name + '_' + '.mha')
sitk.WriteImage(
sitk.GetImageFromArray(np.squeeze(pet_out)),
nm_fig + '/res_pet' + name + '_' + str(ssim) + '.mha')
sitk.WriteImage(
sitk.GetImageFromArray(np.squeeze(asl_out)),
nm_fig + '/res_asl' + name + '_' + str(ssim) + '.mha')
elapsed = time.time() - tic
df = pd.DataFrame(list_ssim_pet, columns=pd.Index(['ssim'],
name='Genus')).round(2)
a = {
'SSIM_HC': list_ssim_HC,
'SSIM_NC': list_ssim_NC,
'MSE_HC': list_mse_HC,
'MSE_NC': list_mse_NC,
'PSNR_HC': list_psnr_HC,
'PSNR_NC': list_psnr_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')
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')
