def __init__(self, data, sample_no, validation_samples, no_sample_per_each_itr,
train_tag, validation_tag, test_tag, img_name, label_name, torso_tag, log_tag, min_range, max_range,
Logs, fold, Server,newdataset=False):
settings.init()
# ==================================
self.train_tag = train_tag
self.validation_tag = validation_tag
self.test_tag = test_tag
self.img_name = img_name
self.label_name = label_name
self.torso_tag = torso_tag
self.data = data
self.display_train_step = 25
# ==================================
settings.validation_totalimg_patch = validation_samples
self.gradients=gradients
# ==================================
self.learning_decay = .95
self.learning_rate = 1E-5
self.beta_rate = 0.05
self.newdataset=newdataset
self.img_padded_size = 519
self.seg_size = 505
self.min_range = min_range
self.max_range = max_range
self.label_patchs_size = 39 # 63
self.patch_window = 53 # 77#89
self.sample_no = sample_no
self.batch_no = 6
self.batch_no_validation = self.batch_no
self.validation_samples = validation_samples
self.display_step = 100
self.display_validation_step = 1
self.total_epochs = 10
self.loss_instance = _loss_func()
self.fold = fold
if Server == 'DL':
self.parent_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/sythesize_code/'
self.data_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/BrainWeb_permutation00_low/'
else:
self.parent_path = '/exports/lkeb-hpc/syousefi/Code/'
self.data_path = '/exports/lkeb-hpc/syousefi/Synth_Data/BrainWeb_permutation2_low/'
self.Logs = Logs
self.no_sample_per_each_itr = no_sample_per_each_itr
self.log_ext = log_tag
self.LOGDIR = self.parent_path + self.Logs + self.log_ext + '/'
self.chckpnt_dir = self.parent_path + self.Logs + self.log_ext + '/unet_checkpoints/'
logger.set_log_file(self.parent_path + self.Logs + self.log_ext + '/log_file' + str(fold))
def test_all_nets():
data = 2
Server = 'DL'
if Server == 'DL':
parent_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/sythesize_code/ASL_LOG/MRI_in/experiment-21/'
data_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/BrainWeb_permutation2_low/'
else:
parent_path = '/exports/lkeb-hpc/syousefi/Code/'
data_path = '/exports/lkeb-hpc/syousefi/Synth_Data/BrainWeb_permutation2_low/'
img_name = ''
label_name = ''
_rd = _read_data(data=data,
reverse=False,
img_name=img_name,
label_name=label_name,
dataset_path=data_path)
'''read path of the images for train, test, and validation'''
train_data, validation_data, test_data = _rd.read_data_path()
chckpnt_dir = parent_path + 'unet_checkpoints/'
result_path = parent_path + 'results/'
if test_vali == 1:
test_set = validation_data
else:
test_set = test_data
# image=tf.placeholder(tf.float32,shape=[batch_no,patch_window,patch_window,patch_window,1])
# label=tf.placeholder(tf.float32,shape=[batch_no_validation,label_patchs_size,label_patchs_size,label_patchs_size,2])
# loss_coef=tf.placeholder(tf.float32,shape=[batch_no_validation,1,1,1])
# img_row1 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row2 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row3 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row4 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row5 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row6 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row7 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row8 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
#
# label1 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label2 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label3 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label4 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label5 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label6 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label7 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label8 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label9 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label10 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label11 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label12 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label13 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label14 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
img_row1 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row2 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row3 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row4 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row5 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row6 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row7 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row8 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
mri_ph = tf.placeholder(tf.float32,
shape=[None, None, None, None, 1],
name='mri')
label1 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label2 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label3 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label4 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label5 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label6 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label7 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
# label8 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
# label9 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
# label10 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
# label11 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
# label12 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
# label13 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
# label14 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
all_loss = tf.placeholder(tf.float32, name='loss')
is_training = tf.placeholder(tf.bool, name='is_training')
input_dim = tf.placeholder(tf.int32, name='input_dim')
# ave_huber = tf.placeholder(tf.float32, name='huber')
densenet = _densenet()
y, degree = densenet.densenet(img_row1=img_row1,
img_row2=img_row2,
img_row3=img_row3,
img_row4=img_row4,
img_row5=img_row5,
img_row6=img_row6,
img_row7=img_row7,
img_row8=img_row8,
input_dim=input_dim,
mri=mri_ph,
is_training=is_training)
loss_instance = _loss_func()
labels = []
labels.append(label1)
labels.append(label2)
labels.append(label3)
labels.append(label4)
labels.append(label5)
labels.append(label6)
labels.append(label7)
logits = []
logits.append(y[:, :, :, :, 0, np.newaxis])
logits.append(y[:, :, :, :, 1, np.newaxis])
logits.append(y[:, :, :, :, 2, np.newaxis])
logits.append(y[:, :, :, :, 3, np.newaxis])
logits.append(y[:, :, :, :, 4, np.newaxis])
logits.append(y[:, :, :, :, 5, np.newaxis])
logits.append(y[:, :, :, :, 6, np.newaxis])
with tf.name_scope('Loss'):
loss_dic = loss_instance.loss_selector('SSIM_perf',
labels=labels,
logits=logits)
cost = tf.reduce_mean(loss_dic["loss"], name="cost")
# ========================================================================
# ave_loss = tf.placeholder(tf.float32, name='loss')
# ave_loss_perf = tf.placeholder(tf.float32, name='loss_perf')
# ave_loss_angio = tf.placeholder(tf.float32, name='loss_angio')
#
# average_gradient_perf = tf.placeholder(tf.float32, name='grad_ave_perf')
# average_gradient_angio = tf.placeholder(tf.float32, name='grad_ave_angio')
#
# ave_huber = tf.placeholder(tf.float32, name='huber')
# restore the model
sess = tf.Session()
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(chckpnt_dir)
saver.restore(sess, ckpt.model_checkpoint_path)
copyfile('./test_synthesize_net_mri.py',
result_path + '/test_synthesize_net_mri.py')
_image_class = image_class(train_data,
bunch_of_images_no=1,
is_training=1,
patch_window=patch_window,
sample_no_per_bunch=1,
label_patch_size=label_patchs_size,
validation_total_sample=0)
learning_rate = 1E-5
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(extra_update_ops):
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
# init = tf.global_variables_initializer()
loss = 0
for img_indx in range(len(test_set)):
crush, noncrush, perf, angio, mri, segmentation, spacing, direction, origin = _image_class.read_image_for_test(
test_set=test_set,
img_indx=img_indx,
input_size=in_dim,
final_layer=final_layer)
[out] = sess.run(
[y],
feed_dict={
img_row1: np.expand_dims(np.expand_dims(crush[0], 0), -1),
img_row2: np.expand_dims(np.expand_dims(noncrush[1], 0), -1),
img_row3: np.expand_dims(np.expand_dims(crush[2], 0), -1),
img_row4: np.expand_dims(np.expand_dims(noncrush[3], 0), -1),
img_row5: np.expand_dims(np.expand_dims(crush[4], 0), -1),
img_row6: np.expand_dims(np.expand_dims(noncrush[5], 0), -1),
img_row7: np.expand_dims(np.expand_dims(crush[6], 0), -1),
img_row8: np.expand_dims(np.expand_dims(noncrush[7], 0), -1),
mri_ph: np.expand_dims(np.expand_dims(mri, 0), -1),
label1: np.expand_dims(np.expand_dims(perf[0], 0), -1),
label2: np.expand_dims(np.expand_dims(perf[1], 0), -1),
label3: np.expand_dims(np.expand_dims(perf[2], 0), -1),
label4: np.expand_dims(np.expand_dims(perf[3], 0), -1),
label5: np.expand_dims(np.expand_dims(perf[4], 0), -1),
label6: np.expand_dims(np.expand_dims(perf[5], 0), -1),
label7: np.expand_dims(np.expand_dims(perf[6], 0), -1),
is_training: False,
input_dim: patch_window,
all_loss: -1.,
})
for i in range(np.shape(out)[-1]):
image = out[0, :, :, :, i]
sitk_image = sitk.GetImageFromArray(image)
res_dir = test_set[img_indx][0][0].split('/')[-2]
if i == 0:
os.mkdir(parent_path + 'results/' + res_dir)
if i < 7:
nm = 'perf'
else:
nm = 'angi'
sitk_image.SetDirection(direction=direction)
sitk_image.SetOrigin(origin=origin)
sitk_image.SetSpacing(spacing=spacing)
sitk.WriteImage(
sitk_image, parent_path + 'results/' + res_dir + '/' + nm +
'_' + str(i % 7) + '.mha')
print(parent_path + 'results/' + res_dir + '/' + nm + '_' +
str(i % 7) + '.mha done!')
for i in range(7):
if i == 0:
os.mkdir(parent_path + 'results/' + res_dir + '/GT/')
# sitk_angio=sitk.GetImageFromArray(angio[i])
# sitk_angio.SetDirection(direction=direction)
# sitk_angio.SetOrigin(origin=origin)
# sitk_angio.SetSpacing(spacing=spacing)
# sitk.WriteImage(sitk_angio, parent_path + 'results/' + res_dir + '/GT/angio_' + str(i) + '.mha')
sitk_perf = sitk.GetImageFromArray(perf[i])
sitk_perf.SetDirection(direction=direction)
sitk_perf.SetOrigin(origin=origin)
sitk_perf.SetSpacing(spacing=spacing)
sitk.WriteImage(
sitk_perf, parent_path + 'results/' + res_dir + '/GT/perf_' +
str(i) + '.mha')
a = 1
# plt.imshow(out[0, int(gt_cube_size / 2), :, :, 0])
# plt.figure()
# loss += loss_train1
# print('Loss_train: ', loss_train1)
print('Total loss: ', loss / len(test_set))
def test_all_nets():
data = 2
Server = 'DL'
if Server == 'DL':
parent_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/sythesize_code/ASL_LOG/multi_stage/experiment-2/'
data_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/BrainWeb_permutation00_low/'
else:
parent_path = '/exports/lkeb-hpc/syousefi/Code/'
data_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/BrainWeb_permutation00_low/'
img_name = ''
label_name = ''
_rd = _read_data(data=data,
reverse=False,
img_name=img_name,
label_name=label_name,
dataset_path=data_path)
'''read path of the images for train, test, and validation'''
train_data, validation_data, test_data = _rd.read_data_path()
chckpnt_dir = parent_path + 'unet_checkpoints/'
result_path = parent_path + 'results/'
batch_no = 1
batch_no_validation = batch_no
# label_patchs_size = 87#39 # 63
# patch_window = 103#53 # 77#89
if test_vali == 1:
test_set = validation_data
else:
test_set = test_data
# ===================================================================================
# img_row1 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row1')
# img_row2 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row2')
# img_row3 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row3')
# img_row4 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row4')
# img_row5 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row5')
# img_row6 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row6')
# img_row7 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row7')
# img_row8 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row8')
#
# mri_ph = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='mri')
#
# segmentation = tf.placeholder(tf.float32,
# shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='segments')
#
# label1 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label1')
# label2 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label2')
# label3 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label3')
# label4 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label4')
# label5 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label5')
# label6 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label6')
# label7 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label7')
# label8 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label8')
# label9 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label9')
# label10 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label10')
# label11 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label11')
# label12 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label12')
# label13 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label13')
# label14 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label14')
img_row1 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row2 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row3 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row4 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row5 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row6 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row7 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row8 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
mri_ph = tf.placeholder(tf.float32,
shape=[None, None, None, None, 1],
name='mri')
# segmentation = tf.placeholder(tf.float32, shape=[None, None, None, None, 1], name='segmentation')
label1 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label2 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label3 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label4 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label5 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label6 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label7 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label8 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label9 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label10 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label11 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label12 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label13 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label14 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
all_loss = tf.placeholder(tf.float32, name='loss')
is_training = tf.placeholder(tf.bool, name='is_training')
input_dim = tf.placeholder(tf.int32, name='input_dim')
# ave_huber = tf.placeholder(tf.float32, name='huber')
multi_stage_densenet = _multi_stage_densenet()
y, loss_upsampling11, loss_upsampling22 = multi_stage_densenet.multi_stage_densenet(
img_row1=img_row1,
img_row2=img_row2,
img_row3=img_row3,
img_row4=img_row4,
img_row5=img_row5,
img_row6=img_row6,
img_row7=img_row7,
img_row8=img_row8,
input_dim=input_dim,
mri=mri_ph,
is_training=is_training)
loss_instance = _loss_func()
labels = []
labels.append(label1)
labels.append(label2)
labels.append(label3)
labels.append(label4)
labels.append(label5)
labels.append(label6)
labels.append(label7)
labels.append(label8)
labels.append(label9)
labels.append(label10)
labels.append(label11)
labels.append(label12)
labels.append(label13)
labels.append(label14)
logits = []
logits.append(y[:, :, :, :, 0, np.newaxis])
logits.append(y[:, :, :, :, 1, np.newaxis])
logits.append(y[:, :, :, :, 2, np.newaxis])
logits.append(y[:, :, :, :, 3, np.newaxis])
logits.append(y[:, :, :, :, 4, np.newaxis])
logits.append(y[:, :, :, :, 5, np.newaxis])
logits.append(y[:, :, :, :, 6, np.newaxis])
logits.append(y[:, :, :, :, 7, np.newaxis])
logits.append(y[:, :, :, :, 8, np.newaxis])
logits.append(y[:, :, :, :, 9, np.newaxis])
logits.append(y[:, :, :, :, 10, np.newaxis])
logits.append(y[:, :, :, :, 11, np.newaxis])
logits.append(y[:, :, :, :, 12, np.newaxis])
logits.append(y[:, :, :, :, 13, np.newaxis])
stage1 = []
stage1.append(loss_upsampling11[:, :, :, :, 0, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 1, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 2, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 3, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 4, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 5, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 6, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 7, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 8, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 9, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 10, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 11, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 12, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 13, np.newaxis])
stage2 = []
stage2.append(loss_upsampling22[:, :, :, :, 0, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 1, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 2, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 3, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 4, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 5, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 6, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 7, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 8, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 9, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 10, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 11, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 12, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 13, np.newaxis])
with tf.name_scope('Loss'):
loss_dic = loss_instance.loss_selector(
'Multistage_ssim_perf_angio_loss',
labels=labels,
logits=logits,
stage1=stage1,
stage2=stage2)
cost = tf.reduce_mean(loss_dic["loss"], name="cost")
# cost_angio = tf.reduce_mean(loss_dic["angio_SSIM"], name="angio_SSIM")
# cost_perf = tf.reduce_mean(loss_dic["perf_SSIM"], name="perf_SSIM")
# ========================================================================
# ave_loss = tf.placeholder(tf.float32, name='loss')
# ave_loss_perf = tf.placeholder(tf.float32, name='loss_perf')
# ave_loss_angio = tf.placeholder(tf.float32, name='loss_angio')
#
# average_gradient_perf = tf.placeholder(tf.float32, name='grad_ave_perf')
# average_gradient_angio = tf.placeholder(tf.float32, name='grad_ave_angio')
#
# ave_huber = tf.placeholder(tf.float32, name='huber')
# restore the model
sess = tf.Session()
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(chckpnt_dir)
saver.restore(sess, ckpt.model_checkpoint_path)
copyfile('./test_synthesize_multistage_perf_angio.py',
result_path + '/test_synthesize_multistage_perf_angio.py')
_image_class = image_class(train_data,
bunch_of_images_no=1,
is_training=1,
patch_window=patch_window,
sample_no_per_bunch=1,
label_patch_size=label_patchs_size,
validation_total_sample=0)
learning_rate = 1E-5
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(extra_update_ops):
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
# init = tf.global_variables_initializer()
dic_perf0 = []
dic_perf1 = []
dic_perf2 = []
dic_perf3 = []
dic_perf4 = []
dic_perf5 = []
dic_perf6 = []
dic_angio0 = []
dic_angio1 = []
dic_angio2 = []
dic_angio3 = []
dic_angio4 = []
dic_angio5 = []
dic_angio6 = []
loss = 0
Elapsed = []
for img_indx in range(2): #len(test_set)):
crush, noncrush, perf, angio, mri, segmentation_, spacing, direction, origin = _image_class.read_image_for_test(
test_set=test_set,
img_indx=img_indx,
input_size=in_dim,
final_layer=final_layer)
t = time.time()
[out] = sess.run(
[y],
feed_dict={
img_row1: np.expand_dims(np.expand_dims(crush[0], 0), -1),
img_row2: np.expand_dims(np.expand_dims(noncrush[1], 0), -1),
img_row3: np.expand_dims(np.expand_dims(crush[2], 0), -1),
img_row4: np.expand_dims(np.expand_dims(noncrush[3], 0), -1),
img_row5: np.expand_dims(np.expand_dims(crush[4], 0), -1),
img_row6: np.expand_dims(np.expand_dims(noncrush[5], 0), -1),
img_row7: np.expand_dims(np.expand_dims(crush[6], 0), -1),
img_row8: np.expand_dims(np.expand_dims(noncrush[7], 0), -1),
mri_ph: np.expand_dims(np.expand_dims(mri, 0), -1),
label1: np.expand_dims(np.expand_dims(perf[0], 0), -1),
label2: np.expand_dims(np.expand_dims(perf[1], 0), -1),
label3: np.expand_dims(np.expand_dims(perf[2], 0), -1),
label4: np.expand_dims(np.expand_dims(perf[3], 0), -1),
label5: np.expand_dims(np.expand_dims(perf[4], 0), -1),
label6: np.expand_dims(np.expand_dims(perf[5], 0), -1),
label7: np.expand_dims(np.expand_dims(perf[6], 0), -1),
label8: np.expand_dims(np.expand_dims(angio[0], 0), -1),
label9: np.expand_dims(np.expand_dims(angio[1], 0), -1),
label10: np.expand_dims(np.expand_dims(angio[2], 0), -1),
label11: np.expand_dims(np.expand_dims(angio[3], 0), -1),
label12: np.expand_dims(np.expand_dims(angio[4], 0), -1),
label13: np.expand_dims(np.expand_dims(angio[5], 0), -1),
label14: np.expand_dims(np.expand_dims(angio[6], 0), -1),
is_training: False,
input_dim: patch_window,
all_loss: -1.,
})
elapsed = time.time() - t
Elapsed.append(elapsed)
print(elapsed)
for i in range(np.shape(out)[-1]):
image = out[0, :, :, :, i]
sitk_image = sitk.GetImageFromArray(image)
res_dir = test_set[img_indx][0][0].split('/')[-2]
if i == 0:
os.mkdir(parent_path + 'results/' + res_dir)
if i < 7:
nm = 'perf'
else:
nm = 'angi'
sitk_image.SetDirection(direction=direction)
sitk_image.SetOrigin(origin=origin)
sitk_image.SetSpacing(spacing=spacing)
sitk.WriteImage(
sitk_image, parent_path + 'results/' + res_dir + '/' + nm +
'_' + str(i % 7) + '.mha')
print(parent_path + 'results/' + res_dir + '/' + nm + '_' +
str(i % 7) + '.mha done!')
for i in range(7):
if i == 0:
os.mkdir(parent_path + 'results/' + res_dir + '/GT/')
sitk_angio = sitk.GetImageFromArray(angio[i])
sitk_angio.SetDirection(direction=direction)
sitk_angio.SetOrigin(origin=origin)
sitk_angio.SetSpacing(spacing=spacing)
sitk.WriteImage(
sitk_angio, parent_path + 'results/' + res_dir + '/GT/angio_' +
str(i) + '.mha')
sitk_perf = sitk.GetImageFromArray(perf[i])
sitk_perf.SetDirection(direction=direction)
sitk_perf.SetOrigin(origin=origin)
sitk_perf.SetSpacing(spacing=spacing)
sitk.WriteImage(
sitk_perf, parent_path + 'results/' + res_dir + '/GT/perf_' +
str(i) + '.mha')
dic_perf0.append(
anly.analysis(out[0, :, :, :, 0], perf[i], 0, max_perf))
dic_perf1.append(
anly.analysis(out[0, :, :, :, 1], perf[i], 0, max_perf))
dic_perf2.append(
anly.analysis(out[0, :, :, :, 2], perf[i], 0, max_perf))
dic_perf3.append(
anly.analysis(out[0, :, :, :, 3], perf[i], 0, max_perf))
dic_perf4.append(
anly.analysis(out[0, :, :, :, 4], perf[i], 0, max_perf))
dic_perf5.append(
anly.analysis(out[0, :, :, :, 5], perf[i], 0, max_perf))
dic_perf6.append(
anly.analysis(out[0, :, :, :, 6], perf[i], 0, max_perf))
dic_angio0.append(
anly.analysis(out[0, :, :, :, 7], angio[i], 0, max_angio))
dic_angio1.append(
anly.analysis(out[0, :, :, :, 8], angio[i], 0, max_angio))
dic_angio2.append(
anly.analysis(out[0, :, :, :, 9], angio[i], 0, max_angio))
dic_angio3.append(
anly.analysis(out[0, :, :, :, 10], angio[i], 0, max_angio))
dic_angio4.append(
anly.analysis(out[0, :, :, :, 11], angio[i], 0, max_angio))
dic_angio5.append(
anly.analysis(out[0, :, :, :, 12], angio[i], 0, max_angio))
dic_angio6.append(
anly.analysis(out[0, :, :, :, 13], angio[i], 0, max_angio))
if img_indx == 0:
headers = dic_perf0[0].keys()
dics = [
dic_perf0, dic_perf1, dic_perf2, dic_perf3, dic_perf4, dic_perf5,
dic_perf6, dic_angio0, dic_angio1, dic_angio2, dic_angio3,
dic_angio4, dic_angio5, dic_angio6
]
# print(np.mean(Elapsed))
# print(np.std(Elapsed))
save_in_xlsx(parent_path, headers, dics=dics)
print('Total loss: ', loss / len(test_set))
def test_all_nets():
data = 2
data_path = '/exports/lkeb-hpc/syousefi/Data/invivo_decomposed/1/'
# parent_path = '/exports/lkeb-hpc/syousefi/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/sythesize_code/ASL_LOG/normal_SSIM_p_a_newdb/experiment-1/'
parent_path = '/exports/lkeb-hpc/syousefi/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/sythesize_code/ASL_LOG/Workshop_log/MSE/experiment-2/'
# data_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/BrainWeb_permutation00_low/'
img_name = ''
label_name = ''
result_dir = 'results_mse/'
_rd = _invivo_read_data(data=data,
reverse=False,
img_name=img_name,
label_name=label_name,
dataset_path=data_path)
'''read path of the images for train, test, and validation'''
test_data = _rd.read_data_path()
chckpnt_dir = parent_path + 'unet_checkpoints/'
result_path = parent_path + result_dir
batch_no = 1
batch_no_validation = batch_no
# label_patchs_size = 87#39 # 63
# patch_window = 103#53 # 77#89
img_row1 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row2 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row3 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row4 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row5 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row6 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row7 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row8 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
mri_ph = tf.placeholder(tf.float32,
shape=[None, None, None, None, 1],
name='mri')
segmentation = tf.placeholder(tf.float32,
shape=[None, None, None, None, 1],
name='segmentation')
label1 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label2 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label3 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label4 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label5 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label6 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label7 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label8 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label9 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label10 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label11 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label12 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label13 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label14 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
all_loss = tf.placeholder(tf.float32, name='loss')
is_training = tf.placeholder(tf.bool, name='is_training')
input_dim = tf.placeholder(tf.int32, name='input_dim')
# ave_huber = tf.placeholder(tf.float32, name='huber')
densenet = _densenet()
y, degree = densenet.densenet(img_row1=img_row1,
img_row2=img_row2,
img_row3=img_row3,
img_row4=img_row4,
img_row5=img_row5,
img_row6=img_row6,
img_row7=img_row7,
img_row8=img_row8,
input_dim=input_dim,
mri=mri_ph,
is_training=is_training)
loss_instance = _loss_func()
labels = []
labels.append(label1)
labels.append(label2)
labels.append(label3)
labels.append(label4)
labels.append(label5)
labels.append(label6)
labels.append(label7)
labels.append(label8)
labels.append(label9)
labels.append(label10)
labels.append(label11)
labels.append(label12)
labels.append(label13)
labels.append(label14)
logits = []
logits.append(y[:, :, :, :, 0, np.newaxis])
logits.append(y[:, :, :, :, 1, np.newaxis])
logits.append(y[:, :, :, :, 2, np.newaxis])
logits.append(y[:, :, :, :, 3, np.newaxis])
logits.append(y[:, :, :, :, 4, np.newaxis])
logits.append(y[:, :, :, :, 5, np.newaxis])
logits.append(y[:, :, :, :, 6, np.newaxis])
logits.append(y[:, :, :, :, 7, np.newaxis])
logits.append(y[:, :, :, :, 8, np.newaxis])
logits.append(y[:, :, :, :, 9, np.newaxis])
logits.append(y[:, :, :, :, 10, np.newaxis])
logits.append(y[:, :, :, :, 11, np.newaxis])
logits.append(y[:, :, :, :, 12, np.newaxis])
logits.append(y[:, :, :, :, 13, np.newaxis])
with tf.name_scope('Loss'):
loss_dic = loss_instance.loss_selector('SSIM_perf_angio',
labels=labels,
logits=logits,
angio_SSIM_weight=1)
cost = tf.reduce_mean(loss_dic["loss"], name="cost")
# cost_angio = tf.reduce_mean(loss_dic["angio_SSIM"], name="angio_SSIM")
# cost_perf = tf.reduce_mean(loss_dic["perf_SSIM"], name="perf_SSIM")
# ========================================================================
# ave_loss = tf.placeholder(tf.float32, name='loss')
# ave_loss_perf = tf.placeholder(tf.float32, name='loss_perf')
# ave_loss_angio = tf.placeholder(tf.float32, name='loss_angio')
#
# average_gradient_perf = tf.placeholder(tf.float32, name='grad_ave_perf')
# average_gradient_angio = tf.placeholder(tf.float32, name='grad_ave_angio')
#
# ave_huber = tf.placeholder(tf.float32, name='huber')
# restore the model
sess = tf.Session()
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(chckpnt_dir)
saver.restore(sess, ckpt.model_checkpoint_path)
# copyfile('./test_synthesize_ssim_perf_angio.py', result_path + '/test_synthesize_ssim_perf_angio.py')
_image_class = invivo_image_class(test_data,
bunch_of_images_no=1,
is_training=1,
patch_window=patch_window,
sample_no_per_bunch=1,
label_patch_size=label_patchs_size,
validation_total_sample=0)
learning_rate = 1E-5
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(extra_update_ops):
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
# init = tf.global_variables_initializer()
dic_perf0 = []
dic_perf1 = []
dic_perf2 = []
dic_perf3 = []
dic_perf4 = []
dic_perf5 = []
dic_perf6 = []
dic_angio0 = []
dic_angio1 = []
dic_angio2 = []
dic_angio3 = []
dic_angio4 = []
dic_angio5 = []
dic_angio6 = []
loss = 0
#min ASL crush : 0.499999, max ASL crush : 0.500000
#min ASL noncrush : 0.499984, max ASL noncrush : 0.500000
#min perfusion : 2.192158, max perfusion : 11.602790
#min angio : 0.000000, max angio : 1309.346558
max_angio = 1309.346558
max_perf = 11.602790
for img_indx in range(len(test_data)):
crush, noncrush, perf, angio, mri, segmentation_, spacing, direction, origin = _image_class.read_image_for_test(
test_set=test_data,
img_indx=img_indx,
input_size=in_dim,
final_layer=final_layer,
invivo=1)
for i in range(8):
crush[i][np.where(crush[i] < 0)] = 0
# crush[i]=np.flipud(crush[i])
crush = crush / np.max(crush)
crush = crush * (0.500000)
for i in range(8):
noncrush[i][np.where(noncrush[i] < 0)] = 0
# noncrush[i] = np.flipud(noncrush[i])
noncrush = noncrush / np.max(noncrush)
noncrush = noncrush * (0.500000)
for i in range(7):
perf[i][np.where(perf[i] < 0)] = 0
# perf[i] = np.flipud(perf[i])
perf = perf / np.max(perf) * max_perf
for i in range(7):
angio[i][np.where(angio[i] < 0)] = 0
# angio[i] = np.flipud(angio[i])
angio = angio / np.max(angio) * max_angio
[out] = sess.run(
[y],
feed_dict={
img_row1: np.expand_dims(np.expand_dims(crush[0], 0), -1),
img_row2: np.expand_dims(np.expand_dims(noncrush[1], 0), -1),
img_row3: np.expand_dims(np.expand_dims(crush[2], 0), -1),
img_row4: np.expand_dims(np.expand_dims(noncrush[3], 0), -1),
img_row5: np.expand_dims(np.expand_dims(crush[4], 0), -1),
img_row6: np.expand_dims(np.expand_dims(noncrush[5], 0), -1),
img_row7: np.expand_dims(np.expand_dims(crush[6], 0), -1),
img_row8: np.expand_dims(np.expand_dims(noncrush[7], 0), -1),
mri_ph: np.expand_dims(np.expand_dims(crush[0], 0),
-1), #useless input
segmentation: np.expand_dims(np.expand_dims(crush[0], 0),
-1), #useless input
label1: np.expand_dims(np.expand_dims(perf[0], 0), -1),
label2: np.expand_dims(np.expand_dims(perf[1], 0), -1),
label3: np.expand_dims(np.expand_dims(perf[2], 0), -1),
label4: np.expand_dims(np.expand_dims(perf[3], 0), -1),
label5: np.expand_dims(np.expand_dims(perf[4], 0), -1),
label6: np.expand_dims(np.expand_dims(perf[5], 0), -1),
label7: np.expand_dims(np.expand_dims(perf[6], 0), -1),
label8: np.expand_dims(np.expand_dims(angio[0], 0), -1),
label9: np.expand_dims(np.expand_dims(angio[1], 0), -1),
label10: np.expand_dims(np.expand_dims(angio[2], 0), -1),
label11: np.expand_dims(np.expand_dims(angio[3], 0), -1),
label12: np.expand_dims(np.expand_dims(angio[4], 0), -1),
label13: np.expand_dims(np.expand_dims(angio[5], 0), -1),
label14: np.expand_dims(np.expand_dims(angio[6], 0), -1),
is_training: False,
input_dim: patch_window,
all_loss: -1.,
})
parent_path = '/exports/lkeb-hpc/syousefi/Data/invivo_decomposed/1/00_PP0_0_cinema1/'
# result_dir='normal_SSIM/'
result_dir = 'results_mse/'
for i in range(np.shape(out)[-1]):
image = out[0, :, :, :, i]
sitk_image = sitk.GetImageFromArray(image)
res_dir = test_data[img_indx][0][0].split('/')[-2]
if i == 0:
os.mkdir(parent_path + result_dir + res_dir)
if i < 7:
nm = 'perf'
else:
nm = 'angi'
sitk_image.SetDirection(direction=direction)
sitk_image.SetOrigin(origin=origin)
sitk_image.SetSpacing(spacing=spacing)
sitk.WriteImage(
sitk_image, parent_path + result_dir + res_dir + '/' + nm +
'_' + str(i % 7) + '.nii')
print(parent_path + result_dir + res_dir + '/' + nm + '_' +
str(i % 7) + '.nii done!')
for i in range(7):
if i == 0:
os.mkdir(parent_path + result_dir + res_dir + '/GT/')
sitk_angio = sitk.GetImageFromArray(angio[i])
sitk_angio.SetDirection(direction=direction)
sitk_angio.SetOrigin(origin=origin)
sitk_angio.SetSpacing(spacing=spacing)
sitk.WriteImage(
sitk_angio, parent_path + result_dir + res_dir + '/GT/angio_' +
str(i) + '.nii')
sitk_perf = sitk.GetImageFromArray(perf[i])
sitk_perf.SetDirection(direction=direction)
sitk_perf.SetOrigin(origin=origin)
sitk_perf.SetSpacing(spacing=spacing)
sitk.WriteImage(
sitk_perf, parent_path + result_dir + res_dir + '/GT/perf_' +
str(i) + '.nii')
dic_perf0.append(
anly.analysis(out[0, :, :, :, 0], perf[i], 0, max_perf))
dic_perf1.append(
anly.analysis(out[0, :, :, :, 1], perf[i], 0, max_perf))
dic_perf2.append(
anly.analysis(out[0, :, :, :, 2], perf[i], 0, max_perf))
dic_perf3.append(
anly.analysis(out[0, :, :, :, 3], perf[i], 0, max_perf))
dic_perf4.append(
anly.analysis(out[0, :, :, :, 4], perf[i], 0, max_perf))
dic_perf5.append(
anly.analysis(out[0, :, :, :, 5], perf[i], 0, max_perf))
dic_perf6.append(
anly.analysis(out[0, :, :, :, 6], perf[i], 0, max_perf))
dic_angio0.append(
anly.analysis(out[0, :, :, :, 7], angio[i], 0, max_angio))
dic_angio1.append(
anly.analysis(out[0, :, :, :, 8], angio[i], 0, max_angio))
dic_angio2.append(
anly.analysis(out[0, :, :, :, 9], angio[i], 0, max_angio))
dic_angio3.append(
anly.analysis(out[0, :, :, :, 10], angio[i], 0, max_angio))
dic_angio4.append(
anly.analysis(out[0, :, :, :, 11], angio[i], 0, max_angio))
dic_angio5.append(
anly.analysis(out[0, :, :, :, 12], angio[i], 0, max_angio))
dic_angio6.append(
anly.analysis(out[0, :, :, :, 13], angio[i], 0, max_angio))
if img_indx == 0:
headers = dic_perf0[0].keys()
dics = [
dic_perf0, dic_perf1, dic_perf2, dic_perf3, dic_perf4, dic_perf5,
dic_perf6, dic_angio0, dic_angio1, dic_angio2, dic_angio3,
dic_angio4, dic_angio5, dic_angio6
]
save_in_xlsx(parent_path, headers, dics=dics)
print('Total loss: ', loss / len(test_data))
def __init__(self, data, sample_no, validation_samples,
no_sample_per_each_itr, train_tag, validation_tag, test_tag,
img_name, label_name, torso_tag, log_tag, min_range,
max_range, Logs, fold, Server):
'''
This function is a constructor for this class
:param data: which dataset should it use
:param sample_no: number of samples which is used for the training process
:param validation_samples: number of samples which is used for the validation process
:param no_sample_per_each_itr: number of samples which is used for each epoch
:param train_tag: tag for training images
:param validation_tag: tag for validation images
:param test_tag: tag for test images
:param img_name: name of the images
:param label_name: name of the labels
:param torso_tag: name of masks if needed
:param log_tag: tag for log dir
:param min_range: min range of images
:param max_range: max range of images
:param Logs: log dir
:param fold: fold no if cross validation is used
:param Server: on which server it is running
:param newdataset: if it is training on the new DB
'''
settings.init()
# ==================================
self.train_tag = train_tag
self.validation_tag = validation_tag
self.test_tag = test_tag
self.img_name = img_name
self.label_name = label_name
self.torso_tag = torso_tag
self.data = data
self.display_train_step = 25
# ==================================
settings.validation_totalimg_patch = validation_samples
self.gradients = gradients
# ==================================
self.learning_decay = .95
self.learning_rate = 1E-4
self.beta_rate = 0.05
self.img_padded_size = 519
self.seg_size = 505
self.min_range = min_range
self.max_range = max_range
self.label_patchs_size = 39 # 63 #input size
self.patch_window = 53 # 77#89 #output size
self.sample_no = sample_no
self.batch_no = 6
self.batch_no_validation = self.batch_no
self.validation_samples = validation_samples
self.display_step = 100
self.display_validation_step = 1
self.total_epochs = 10
self.loss_instance = _loss_func()
self.fold = fold
self.quantifications = quantifications()
if Server == 'DL':
self.parent_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/sythesize_code/'
self.data_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/BrainWeb_permutation00_low/'
else:
self.parent_path = '/exports/lkeb-hpc/syousefi/Code/'
self.data_path = '/exports/lkeb-hpc/syousefi/Synth_Data/BrainWeb_permutation00_low/'
self.Logs = Logs
self.no_sample_per_each_itr = no_sample_per_each_itr
self.log_ext = log_tag
self.LOGDIR = self.parent_path + self.Logs + self.log_ext + '/'
self.chckpnt_dir = self.parent_path + self.Logs + self.log_ext + '/unet_checkpoints/'
logger.set_log_file(self.parent_path + self.Logs + self.log_ext +
'/log_file' + str(fold))
def test_all_nets(newdataset):
data = 2
Server = 'shark'
if Server == 'DL':
parent_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/sythesize_code/'
if newdataset == True:
data_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/BrainWeb_permutation00_low/'
else:
data_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/BrainWeb_permutation2_low/'
else:
parent_path = '/exports/lkeb-hpc/syousefi/Code/'
if newdataset == True:
data_path = '/exports/lkeb-hpc/syousefi/Synth_Data/BrainWeb_permutation00_low/'
else:
data_path = '/exports/lkeb-hpc/syousefi/Synth_Data/BrainWeb_permutation2_low/'
img_name = ''
label_name = ''
_rd = _read_data(data=data,
img_name=img_name,
label_name=label_name,
dataset_path=data_path)
'''read path of the images for train, test, and validation'''
train_data, validation_data, test_data = _rd.read_data_path()
# parent_path='/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/sythesize_code/Log/synth-forked_synthesizing_net_rotate-1/'
parent_path = '/exports/lkeb-hpc/syousefi/Code/ASL_LOG/debug_Log/synth-12/'
chckpnt_dir = parent_path + 'unet_checkpoints/'
if test_vali == 1:
test_set = validation_data
result_path = parent_path + 'results/'
elif test_vali == 2:
test_set = train_data
result_path = parent_path + 'results_tr/'
else:
test_set = test_data
result_path = parent_path + 'results_vali/'
# image=tf.placeholder(tf.float32,shape=[batch_no,patch_window,patch_window,patch_window,1])
# label=tf.placeholder(tf.float32,shape=[batch_no_validation,label_patchs_size,label_patchs_size,label_patchs_size,2])
# loss_coef=tf.placeholder(tf.float32,shape=[batch_no_validation,1,1,1])
# img_row1 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row2 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row3 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row4 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row5 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row6 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row7 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
# img_row8 = tf.placeholder(tf.float32, shape=[batch_no,patch_window,patch_window,patch_window, 1])
#
# label1 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label2 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label3 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label4 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label5 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label6 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label7 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label8 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label9 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label10 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label11 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label12 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label13 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
# label14 = tf.placeholder(tf.float32, shape=[batch_no,label_patchs_size,label_patchs_size,label_patchs_size, 1])
img_row1 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row2 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row3 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row4 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row5 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row6 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row7 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row8 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label1 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label2 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label3 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label4 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label5 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label6 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label7 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label8 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label9 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label10 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label11 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label12 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label13 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label14 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
is_training = tf.placeholder(tf.bool, name='is_training')
input_dim = tf.placeholder(tf.int32, name='input_dim')
# ave_huber = tf.placeholder(tf.float32, name='huber')
forked_densenet = _forked_densenet()
y, img_row1, img_row2, img_row3, img_row4, \
img_row5, img_row6, img_row7, img_row8 = \
forked_densenet.densenet(img_row1=img_row1, img_row2=img_row2, img_row3=img_row3, img_row4=img_row4,
img_row5=img_row5,
img_row6=img_row6, img_row7=img_row7, img_row8=img_row8, input_dim=input_dim,
is_training=is_training)
loss_instance = _loss_func()
with tf.name_scope('averaged_mean_squared_error'):
[
_loss, _ssim, _huber, _ssim_angio, _ssim_perf, _huber_angio,
_huber_perf, perf_loss, angio_loss
] = loss_instance.averaged_SSIM_huber(
label1=label1,
label2=label2,
label3=label3,
label4=label4,
label5=label5,
label6=label6,
label7=label7,
label8=label8,
label9=label9,
label10=label10,
label11=label11,
label12=label12,
label13=label13,
label14=label14,
logit1=y[:, :, :, :, 0, np.newaxis],
logit2=y[:, :, :, :, 1, np.newaxis],
logit3=y[:, :, :, :, 2, np.newaxis],
logit4=y[:, :, :, :, 3, np.newaxis],
logit5=y[:, :, :, :, 4, np.newaxis],
logit6=y[:, :, :, :, 5, np.newaxis],
logit7=y[:, :, :, :, 6, np.newaxis],
logit8=y[:, :, :, :, 7, np.newaxis],
logit9=y[:, :, :, :, 8, np.newaxis],
logit10=y[:, :, :, :, 9, np.newaxis],
logit11=y[:, :, :, :, 10, np.newaxis],
logit12=y[:, :, :, :, 11, np.newaxis],
logit13=y[:, :, :, :, 12, np.newaxis],
logit14=y[:, :, :, :, 13, np.newaxis])
cost = tf.reduce_mean(_loss, name="cost")
ssim_cost = tf.reduce_mean(_ssim, name="ssim_cost")
huber_cost = tf.reduce_mean(_huber, name="huber_cost")
ssim_angio = tf.reduce_mean(_ssim_angio, name="ssim_angio")
ssim_perf = tf.reduce_mean(_ssim_perf, name="ssim_perf")
huber_angio = tf.reduce_mean(_huber_angio, name="huber_angio")
huber_perf = tf.reduce_mean(_huber_perf, name="huber_perf")
# ========================================================================
ave_loss = tf.placeholder(tf.float32, name='loss')
ave_loss_perf = tf.placeholder(tf.float32, name='loss_perf')
ave_loss_angio = tf.placeholder(tf.float32, name='loss_angio')
average_gradient_perf = tf.placeholder(tf.float32, name='grad_ave_perf')
average_gradient_angio = tf.placeholder(tf.float32, name='grad_ave_angio')
ave_huber = tf.placeholder(tf.float32, name='huber')
# restore the model
sess = tf.Session()
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(chckpnt_dir)
saver.restore(sess, ckpt.model_checkpoint_path)
copyfile('./test_synthesize_net2.py',
result_path + '/test_synthesize_net2.py')
_image_class = image_class(train_data,
bunch_of_images_no=1,
is_training=1,
patch_window=patch_window,
sample_no_per_bunch=1,
label_patch_size=label_patchs_size,
validation_total_sample=0)
learning_rate = 1E-5
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# with tf.control_dependencies(extra_update_ops):
# optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
# init = tf.global_variables_initializer()
loss = 0
for img_indx in range(len(test_set)):
crush, noncrush, perf, angio, spacing, direction, origin = _image_class.read_image_for_test(
test_set=test_set,
img_indx=img_indx,
input_size=in_dim,
final_layer=final_layer)
[
loss_train1,
out,
] = sess.run(
[
cost,
y,
],
feed_dict={
img_row1:
np.expand_dims(np.expand_dims(crush[0], axis=-1), axis=0),
img_row2:
np.expand_dims(np.expand_dims(noncrush[1], axis=-1), axis=0),
img_row3:
np.expand_dims(np.expand_dims(crush[2], axis=-1), axis=0),
img_row4:
np.expand_dims(np.expand_dims(noncrush[3], axis=-1), axis=0),
img_row5:
np.expand_dims(np.expand_dims(crush[4], axis=-1), axis=0),
img_row6:
np.expand_dims(np.expand_dims(noncrush[5], axis=-1), axis=0),
img_row7:
np.expand_dims(np.expand_dims(crush[6], axis=-1), axis=0),
img_row8:
np.expand_dims(np.expand_dims(noncrush[7], axis=-1), axis=0),
label1:
np.expand_dims(np.expand_dims(perf[0], axis=-1), axis=0),
label2:
np.expand_dims(np.expand_dims(perf[1], axis=-1), axis=0),
label3:
np.expand_dims(np.expand_dims(perf[2], axis=-1), axis=0),
label4:
np.expand_dims(np.expand_dims(perf[3], axis=-1), axis=0),
label5:
np.expand_dims(np.expand_dims(perf[4], axis=-1), axis=0),
label6:
np.expand_dims(np.expand_dims(perf[5], axis=-1), axis=0),
label7:
np.expand_dims(np.expand_dims(perf[6], axis=-1), axis=0),
label8:
np.expand_dims(np.expand_dims(angio[0], axis=-1), axis=0),
label9:
np.expand_dims(np.expand_dims(angio[1], axis=-1), axis=0),
label10:
np.expand_dims(np.expand_dims(angio[2], axis=-1), axis=0),
label11:
np.expand_dims(np.expand_dims(angio[3], axis=-1), axis=0),
label12:
np.expand_dims(np.expand_dims(angio[4], axis=-1), axis=0),
label13:
np.expand_dims(np.expand_dims(angio[5], axis=-1), axis=0),
label14:
np.expand_dims(np.expand_dims(angio[6], axis=-1), axis=0),
is_training:
False,
input_dim:
patch_window,
ave_loss:
-1,
ave_loss_perf:
-1,
ave_loss_angio:
-1,
average_gradient_perf:
-1,
average_gradient_angio:
-1
})
for i in range(np.shape(out)[-1]):
image = out[0, :, :, :, i]
sitk_image = sitk.GetImageFromArray(image)
res_dir = test_set[img_indx][0][0].split('/')[-2]
if i == 0 and test_vali == 1:
os.mkdir(parent_path + 'results/' + res_dir)
elif i == 0 and test_vali == 2:
os.mkdir(parent_path + 'results_tr/' + res_dir)
elif i == 0 and test_vali == 3:
os.mkdir(parent_path + 'results_vali/' + res_dir)
if i < 7:
nm = 'perf'
else:
nm = 'angi'
sitk_image.SetDirection(direction=direction)
sitk_image.SetOrigin(origin=origin)
sitk_image.SetSpacing(spacing=spacing)
if test_vali == 1:
sitk.WriteImage(
sitk_image, parent_path + 'results/' + res_dir + '/' + nm +
'_' + str(i % 7) + '.mha')
print(parent_path + 'results/' + res_dir + '/' + nm + '_' +
str(i % 7) + '.mha done!')
elif test_vali == 2:
sitk.WriteImage(
sitk_image, parent_path + 'results_tr/' + res_dir + '/' +
nm + '_' + str(i % 7) + '.mha')
print(parent_path + 'results_tr/' + res_dir + '/' + nm + '_' +
str(i % 7) + '.mha done!')
else:
sitk.WriteImage(
sitk_image, parent_path + 'results_vali/' + res_dir + '/' +
nm + '_' + str(i % 7) + '.mha')
print(parent_path + 'results_vali/' + res_dir + '/' + nm +
'_' + str(i % 7) + '.mha done!')
for i in range(7):
if i == 0 and test_vali == 1:
os.mkdir(parent_path + 'results/' + res_dir + '/GT/')
if i == 0 and test_vali == 2:
os.mkdir(parent_path + 'results_tr/' + res_dir + '/GT/')
if i == 0 and test_vali == 3:
os.mkdir(parent_path + 'results_vali/' + res_dir + '/GT/')
sitk_angio = sitk.GetImageFromArray(angio[i])
sitk_angio.SetDirection(direction=direction)
sitk_angio.SetOrigin(origin=origin)
sitk_angio.SetSpacing(spacing=spacing)
sitk_perf = sitk.GetImageFromArray(perf[i])
sitk_perf.SetDirection(direction=direction)
sitk_perf.SetOrigin(origin=origin)
sitk_perf.SetSpacing(spacing=spacing)
if test_vali == 1:
sitk.WriteImage(
sitk_angio, parent_path + 'results/' + res_dir +
'/GT/angio_' + str(i) + '.mha')
sitk.WriteImage(
sitk_perf, parent_path + 'results/' + res_dir +
'/GT/perf_' + str(i) + '.mha')
elif test_vali == 2:
sitk.WriteImage(
sitk_angio, parent_path + 'results_tr/' + res_dir +
'/GT/angio_' + str(i) + '.mha')
sitk.WriteImage(
sitk_perf, parent_path + 'results_tr/' + res_dir +
'/GT/perf_' + str(i) + '.mha')
else:
sitk.WriteImage(
sitk_angio, parent_path + 'results_vali/' + res_dir +
'/GT/angio_' + str(i) + '.mha')
sitk.WriteImage(
sitk_perf, parent_path + 'results_vali/' + res_dir +
'/GT/perf_' + str(i) + '.mha')
a = 1
# plt.imshow(out[0, int(gt_cube_size / 2), :, :, 0])
# plt.figure()
loss += loss_train1
print('Loss_train: ', loss_train1)
print('Total loss: ', loss / len(test_set))
def test_all_nets(num, test_set):
chckpnt_dir = parent_path + 'unet_checkpoints/'
img_row1 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row2 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row3 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row4 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row5 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row6 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row7 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row8 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
mri_ph = tf.placeholder(tf.float32,
shape=[None, None, None, None, 1],
name='mri')
label1 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label2 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label3 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label4 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label5 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label6 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label7 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label8 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label9 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label10 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label11 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label12 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label13 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label14 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
all_loss = tf.placeholder(tf.float32, name='loss')
is_training = tf.placeholder(tf.bool, name='is_training')
input_dim = tf.placeholder(tf.int32, name='input_dim')
multi_stage_densenet = _multi_stage_densenet()
y, loss_upsampling11, loss_upsampling22 = multi_stage_densenet.multi_stage_densenet(
img_row1=img_row1,
img_row2=img_row2,
img_row3=img_row3,
img_row4=img_row4,
img_row5=img_row5,
img_row6=img_row6,
img_row7=img_row7,
img_row8=img_row8,
input_dim=input_dim,
mri=mri_ph,
is_training=is_training)
loss_instance = _loss_func()
labels = []
labels.append(label1)
labels.append(label2)
labels.append(label3)
labels.append(label4)
labels.append(label5)
labels.append(label6)
labels.append(label7)
labels.append(label8)
labels.append(label9)
labels.append(label10)
labels.append(label11)
labels.append(label12)
labels.append(label13)
labels.append(label14)
logits = []
logits.append(y[:, :, :, :, 0, np.newaxis])
logits.append(y[:, :, :, :, 1, np.newaxis])
logits.append(y[:, :, :, :, 2, np.newaxis])
logits.append(y[:, :, :, :, 3, np.newaxis])
logits.append(y[:, :, :, :, 4, np.newaxis])
logits.append(y[:, :, :, :, 5, np.newaxis])
logits.append(y[:, :, :, :, 6, np.newaxis])
logits.append(y[:, :, :, :, 7, np.newaxis])
logits.append(y[:, :, :, :, 8, np.newaxis])
logits.append(y[:, :, :, :, 9, np.newaxis])
logits.append(y[:, :, :, :, 10, np.newaxis])
logits.append(y[:, :, :, :, 11, np.newaxis])
logits.append(y[:, :, :, :, 12, np.newaxis])
logits.append(y[:, :, :, :, 13, np.newaxis])
stage1 = []
stage1.append(loss_upsampling11[:, :, :, :, 0, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 1, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 2, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 3, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 4, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 5, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 6, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 7, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 8, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 9, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 10, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 11, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 12, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 13, np.newaxis])
stage2 = []
stage2.append(loss_upsampling22[:, :, :, :, 0, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 1, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 2, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 3, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 4, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 5, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 6, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 7, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 8, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 9, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 10, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 11, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 12, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 13, np.newaxis])
with tf.name_scope('Loss'):
loss_dic = loss_instance.loss_selector(
'Multistage_ssim_perf_angio_loss',
labels=labels,
logits=logits,
stage1=stage1,
stage2=stage2)
cost = tf.reduce_mean(loss_dic["loss"], name="cost")
sess = tf.Session()
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(chckpnt_dir)
model_path = ckpt.model_checkpoint_path.rsplit(
'/',
1)[0] + '/unet_inter_epoch0_point' + str(num) + '.ckpt-' + str(num)
saver.restore(sess, model_path)
# copyfile('./test_synthesize_multistage_perf_angio.py', result_path + '/test_synthesize_multistage_perf_angio.py')
_image_class = image_class(train_data,
bunch_of_images_no=1,
is_training=1,
patch_window=patch_window,
sample_no_per_bunch=1,
label_patch_size=label_patchs_size,
validation_total_sample=0)
learning_rate = 1E-5
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(extra_update_ops):
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
# init = tf.global_variables_initializer()
loss = 0
for img_indx in range(len(test_set[0:1])):
crush, noncrush, perf, angio, mri, segmentation_, spacing, direction, origin = _image_class.read_image_for_test(
test_set=test_set,
img_indx=img_indx,
input_size=in_dim,
final_layer=final_layer)
[out] = sess.run(
[y],
feed_dict={
img_row1: np.expand_dims(np.expand_dims(crush[0], 0), -1),
img_row2: np.expand_dims(np.expand_dims(noncrush[1], 0), -1),
img_row3: np.expand_dims(np.expand_dims(crush[2], 0), -1),
img_row4: np.expand_dims(np.expand_dims(noncrush[3], 0), -1),
img_row5: np.expand_dims(np.expand_dims(crush[4], 0), -1),
img_row6: np.expand_dims(np.expand_dims(noncrush[5], 0), -1),
img_row7: np.expand_dims(np.expand_dims(crush[6], 0), -1),
img_row8: np.expand_dims(np.expand_dims(noncrush[7], 0), -1),
mri_ph: np.expand_dims(np.expand_dims(mri, 0), -1),
label1: np.expand_dims(np.expand_dims(perf[0], 0), -1),
label2: np.expand_dims(np.expand_dims(perf[1], 0), -1),
label3: np.expand_dims(np.expand_dims(perf[2], 0), -1),
label4: np.expand_dims(np.expand_dims(perf[3], 0), -1),
label5: np.expand_dims(np.expand_dims(perf[4], 0), -1),
label6: np.expand_dims(np.expand_dims(perf[5], 0), -1),
label7: np.expand_dims(np.expand_dims(perf[6], 0), -1),
label8: np.expand_dims(np.expand_dims(angio[0], 0), -1),
label9: np.expand_dims(np.expand_dims(angio[1], 0), -1),
label10: np.expand_dims(np.expand_dims(angio[2], 0), -1),
label11: np.expand_dims(np.expand_dims(angio[3], 0), -1),
label12: np.expand_dims(np.expand_dims(angio[4], 0), -1),
label13: np.expand_dims(np.expand_dims(angio[5], 0), -1),
label14: np.expand_dims(np.expand_dims(angio[6], 0), -1),
is_training: False,
input_dim: patch_window,
all_loss: -1.,
})
for i in range(np.shape(out)[-1]):
image = out[0, :, :, :, i]
gt = sitk.GetArrayFromImage(sitk.GetImageFromArray(perf[i]))
dic = analysis.analysis(result=image, gt=gt, min=0, max=max_perf)
print(dic)
# for i in range(np.shape(out)[-1]):
# image = out[0, :, :, :, i]
# sitk_image = sitk.GetImageFromArray(image)
# res_dir = test_set[img_indx][0][0].split('/')[-2]
# if i == 0:
# os.mkdir(parent_path + 'results/' + res_dir)
# if i < 7:
# nm = 'perf'
# else:
# nm = 'angi'
# sitk_image.SetDirection(direction=direction)
# sitk_image.SetOrigin(origin=origin)
# sitk_image.SetSpacing(spacing=spacing)
# sitk.WriteImage(sitk_image, parent_path + 'results/' + res_dir + '/' + nm + '_' + str(i % 7) + '.mha')
# print(parent_path + 'results/' + res_dir + '/' + nm + '_' + str(i % 7) + '.mha done!')
# for i in range(7):
# if i == 0:
# os.mkdir(parent_path + 'results/' + res_dir + '/GT/')
# sitk_angio = sitk.GetImageFromArray(angio[i])
# sitk_angio.SetDirection(direction=direction)
# sitk_angio.SetOrigin(origin=origin)
# sitk_angio.SetSpacing(spacing=spacing)
# sitk.WriteImage(sitk_angio, parent_path + 'results/' + res_dir + '/GT/angio_' + str(i) + '.mha')
#
# sitk_perf = sitk.GetImageFromArray(perf[i])
# sitk_perf.SetDirection(direction=direction)
# sitk_perf.SetOrigin(origin=origin)
# sitk_perf.SetSpacing(spacing=spacing)
# sitk.WriteImage(sitk_perf, parent_path + 'results/' + res_dir + '/GT/perf_' + str(i) + '.mha')
a = 1
# plt.imshow(out[0, int(gt_cube_size / 2), :, :, 0])
# plt.figure()
# loss += loss_train1
# print('Loss_train: ', loss_train1)
print('Total loss: ', loss / len(test_set))
def test_all_nets():
data = 2
Server = 'DL'
if Server == 'DL':
parent_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/sythesize_code/ASL_LOG/multi_stage/experiment-2/'
data_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/BrainWeb_permutation00_low/'
else:
parent_path = '/exports/lkeb-hpc/syousefi/Code/'
data_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/BrainWeb_permutation00_low/'
img_name = ''
label_name = ''
_rd = _read_data(data=data,
reverse=False,
img_name=img_name,
label_name=label_name,
dataset_path=data_path)
'''read path of the images for train, test, and validation'''
train_data, validation_data, test_data = _rd.read_data_path()
chckpnt_dir = parent_path + 'unet_checkpoints/'
result_path = parent_path + 'results/'
batch_no = 1
batch_no_validation = batch_no
# label_patchs_size = 87#39 # 63
# patch_window = 103#53 # 77#89
if test_vali == 1:
test_set = validation_data
else:
test_set = test_data
# ===================================================================================
# img_row1 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row1')
# img_row2 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row2')
# img_row3 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row3')
# img_row4 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row4')
# img_row5 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row5')
# img_row6 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row6')
# img_row7 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row7')
# img_row8 = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='img_row8')
#
# mri_ph = tf.placeholder(tf.float32,
# shape=[batch_no, patch_window, patch_window, patch_window, 1],
# name='mri')
#
# segmentation = tf.placeholder(tf.float32,
# shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='segments')
#
# label1 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label1')
# label2 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label2')
# label3 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label3')
# label4 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label4')
# label5 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label5')
# label6 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label6')
# label7 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label7')
# label8 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label8')
# label9 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label9')
# label10 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label10')
# label11 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label11')
# label12 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label12')
# label13 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label13')
# label14 = tf.placeholder(tf.float32, shape=[batch_no, label_patchs_size, label_patchs_size,
# label_patchs_size, 1], name='label14')
img_row1 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row2 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row3 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row4 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row5 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row6 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row7 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
img_row8 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
mri_ph = tf.placeholder(tf.float32,
shape=[None, None, None, None, 1],
name='mri')
# segmentation = tf.placeholder(tf.float32, shape=[None, None, None, None, 1], name='segmentation')
label1 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label2 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label3 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label4 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label5 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label6 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label7 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label8 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label9 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label10 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label11 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label12 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label13 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
label14 = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
all_loss = tf.placeholder(tf.float32, name='loss')
is_training = tf.placeholder(tf.bool, name='is_training')
input_dim = tf.placeholder(tf.int32, name='input_dim')
# ave_huber = tf.placeholder(tf.float32, name='huber')
multi_stage_densenet = _multi_stage_densenet()
y, loss_upsampling11, loss_upsampling22 = multi_stage_densenet.multi_stage_densenet(
img_row1=img_row1,
img_row2=img_row2,
img_row3=img_row3,
img_row4=img_row4,
img_row5=img_row5,
img_row6=img_row6,
img_row7=img_row7,
img_row8=img_row8,
input_dim=input_dim,
mri=mri_ph,
is_training=is_training)
loss_instance = _loss_func()
labels = []
labels.append(label1)
labels.append(label2)
labels.append(label3)
labels.append(label4)
labels.append(label5)
labels.append(label6)
labels.append(label7)
labels.append(label8)
labels.append(label9)
labels.append(label10)
labels.append(label11)
labels.append(label12)
labels.append(label13)
labels.append(label14)
logits = []
logits.append(y[:, :, :, :, 0, np.newaxis])
logits.append(y[:, :, :, :, 1, np.newaxis])
logits.append(y[:, :, :, :, 2, np.newaxis])
logits.append(y[:, :, :, :, 3, np.newaxis])
logits.append(y[:, :, :, :, 4, np.newaxis])
logits.append(y[:, :, :, :, 5, np.newaxis])
logits.append(y[:, :, :, :, 6, np.newaxis])
logits.append(y[:, :, :, :, 7, np.newaxis])
logits.append(y[:, :, :, :, 8, np.newaxis])
logits.append(y[:, :, :, :, 9, np.newaxis])
logits.append(y[:, :, :, :, 10, np.newaxis])
logits.append(y[:, :, :, :, 11, np.newaxis])
logits.append(y[:, :, :, :, 12, np.newaxis])
logits.append(y[:, :, :, :, 13, np.newaxis])
stage1 = []
stage1.append(loss_upsampling11[:, :, :, :, 0, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 1, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 2, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 3, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 4, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 5, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 6, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 7, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 8, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 9, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 10, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 11, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 12, np.newaxis])
stage1.append(loss_upsampling11[:, :, :, :, 13, np.newaxis])
stage2 = []
stage2.append(loss_upsampling22[:, :, :, :, 0, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 1, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 2, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 3, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 4, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 5, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 6, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 7, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 8, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 9, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 10, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 11, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 12, np.newaxis])
stage2.append(loss_upsampling22[:, :, :, :, 13, np.newaxis])
with tf.name_scope('Loss'):
loss_dic = loss_instance.loss_selector(
'Multistage_ssim_perf_angio_loss',
labels=labels,
logits=logits,
stage1=stage1,
stage2=stage2)
cost = tf.reduce_mean(loss_dic["loss"], name="cost")
# cost_angio = tf.reduce_mean(loss_dic["angio_SSIM"], name="angio_SSIM")
# cost_perf = tf.reduce_mean(loss_dic["perf_SSIM"], name="perf_SSIM")
# ========================================================================
# ave_loss = tf.placeholder(tf.float32, name='loss')
# ave_loss_perf = tf.placeholder(tf.float32, name='loss_perf')
# ave_loss_angio = tf.placeholder(tf.float32, name='loss_angio')
#
# average_gradient_perf = tf.placeholder(tf.float32, name='grad_ave_perf')
# average_gradient_angio = tf.placeholder(tf.float32, name='grad_ave_angio')
#
# ave_huber = tf.placeholder(tf.float32, name='huber')
# restore the model
sess = tf.Session()
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(chckpnt_dir)
saver.restore(sess, ckpt.model_checkpoint_path)
_image_class = image_class(train_data,
bunch_of_images_no=1,
is_training=1,
patch_window=patch_window,
sample_no_per_bunch=1,
label_patch_size=label_patchs_size,
validation_total_sample=0)
learning_rate = 1E-5
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(extra_update_ops):
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
# init = tf.global_variables_initializer()
loss = 0
Elapsed = []
for img_indx in range(len(test_set)):
crush, noncrush, perf, angio, mri, segmentation_, spacing, direction, origin = _image_class.read_image_for_test(
test_set=test_set,
img_indx=img_indx,
input_size=in_dim,
final_layer=final_layer)
t = time.time()
[out] = sess.run(
[y],
feed_dict={
img_row1: np.expand_dims(np.expand_dims(crush[0], 0), -1),
img_row2: np.expand_dims(np.expand_dims(noncrush[1], 0), -1),
img_row3: np.expand_dims(np.expand_dims(crush[2], 0), -1),
img_row4: np.expand_dims(np.expand_dims(noncrush[3], 0), -1),
img_row5: np.expand_dims(np.expand_dims(crush[4], 0), -1),
img_row6: np.expand_dims(np.expand_dims(noncrush[5], 0), -1),
img_row7: np.expand_dims(np.expand_dims(crush[6], 0), -1),
img_row8: np.expand_dims(np.expand_dims(noncrush[7], 0), -1),
mri_ph: np.expand_dims(np.expand_dims(mri, 0), -1),
label1: np.expand_dims(np.expand_dims(perf[0], 0), -1),
label2: np.expand_dims(np.expand_dims(perf[1], 0), -1),
label3: np.expand_dims(np.expand_dims(perf[2], 0), -1),
label4: np.expand_dims(np.expand_dims(perf[3], 0), -1),
label5: np.expand_dims(np.expand_dims(perf[4], 0), -1),
label6: np.expand_dims(np.expand_dims(perf[5], 0), -1),
label7: np.expand_dims(np.expand_dims(perf[6], 0), -1),
label8: np.expand_dims(np.expand_dims(angio[0], 0), -1),
label9: np.expand_dims(np.expand_dims(angio[1], 0), -1),
label10: np.expand_dims(np.expand_dims(angio[2], 0), -1),
label11: np.expand_dims(np.expand_dims(angio[3], 0), -1),
label12: np.expand_dims(np.expand_dims(angio[4], 0), -1),
label13: np.expand_dims(np.expand_dims(angio[5], 0), -1),
label14: np.expand_dims(np.expand_dims(angio[6], 0), -1),
is_training: False,
input_dim: patch_window,
all_loss: -1.,
})
elapsed = time.time() - t
Elapsed.append(elapsed)
print(elapsed)
print('MEAN:')
print(np.mean(Elapsed))
print('STD:')
print(np.std(Elapsed))
print('Total loss: ', loss / len(test_set))
def test_all_nets():
data = 2
Server = 'DL'
if Server == 'DL':
parent_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/sythesize_code/ASL_LOG/Log_perceptual/regularization/perceptual-0/'
data_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/BrainWeb_permutation00_low/'
else:
parent_path = '/exports/lkeb-hpc/syousefi/Code/'
data_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/BrainWeb_permutation00_low/'
img_name = ''
label_name = ''
_rd = _read_data(data=data,
reverse=False,
img_name=img_name,
label_name=label_name,
dataset_path=data_path)
'''read path of the images for train, test, and validation'''
train_data, validation_data, test_data = _rd.read_data_path()
chckpnt_dir = parent_path + 'unet_checkpoints/'
result_path = parent_path + 'results/'
batch_no = 1
batch_no_validation = batch_no
# label_patchs_size = 87#39 # 63
# patch_window = 103#53 # 77#89
if test_vali == 1:
test_set = validation_data
else:
test_set = test_data
# ===================================================================================
img_row1 = tf.placeholder(
tf.float32,
shape=[batch_no, patch_window, patch_window, patch_window, 1],
name='img_row1')
img_row2 = tf.placeholder(
tf.float32,
shape=[batch_no, patch_window, patch_window, patch_window, 1],
name='img_row2')
img_row3 = tf.placeholder(
tf.float32,
shape=[batch_no, patch_window, patch_window, patch_window, 1],
name='img_row3')
img_row4 = tf.placeholder(
tf.float32,
shape=[batch_no, patch_window, patch_window, patch_window, 1],
name='img_row4')
img_row5 = tf.placeholder(
tf.float32,
shape=[batch_no, patch_window, patch_window, patch_window, 1],
name='img_row5')
img_row6 = tf.placeholder(
tf.float32,
shape=[batch_no, patch_window, patch_window, patch_window, 1],
name='img_row6')
img_row7 = tf.placeholder(
tf.float32,
shape=[batch_no, patch_window, patch_window, patch_window, 1],
name='img_row7')
img_row8 = tf.placeholder(
tf.float32,
shape=[batch_no, patch_window, patch_window, patch_window, 1],
name='img_row8')
mri_ph = tf.placeholder(
tf.float32,
shape=[batch_no, patch_window, patch_window, patch_window, 1],
name='mri')
label1 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size, label_patchs_size,
label_patchs_size, 1
],
name='label1')
label2 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size, label_patchs_size,
label_patchs_size, 1
],
name='label2')
label3 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size, label_patchs_size,
label_patchs_size, 1
],
name='label3')
label4 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size, label_patchs_size,
label_patchs_size, 1
],
name='label4')
label5 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size, label_patchs_size,
label_patchs_size, 1
],
name='label5')
label6 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size, label_patchs_size,
label_patchs_size, 1
],
name='label6')
label7 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size, label_patchs_size,
label_patchs_size, 1
],
name='label7')
label8 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size, label_patchs_size,
label_patchs_size, 1
],
name='label8')
label9 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size, label_patchs_size,
label_patchs_size, 1
],
name='label9')
label10 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size,
label_patchs_size, label_patchs_size, 1
],
name='label10')
label11 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size,
label_patchs_size, label_patchs_size, 1
],
name='label11')
label12 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size,
label_patchs_size, label_patchs_size, 1
],
name='label12')
label13 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size,
label_patchs_size, label_patchs_size, 1
],
name='label13')
label14 = tf.placeholder(tf.float32,
shape=[
batch_no, label_patchs_size,
label_patchs_size, label_patchs_size, 1
],
name='label14')
is_training = tf.placeholder(tf.bool, name='is_training')
input_dim = tf.placeholder(tf.int32, name='input_dim')
perf_vgg_loss_tens = tf.placeholder(tf.float32, name='VGG_perf')
angio_vgg_loss_tens = tf.placeholder(tf.float32, name='VGG_angio')
perf_vgg_tens0 = tf.placeholder(tf.float32, name='vgg_perf0')
perf_vgg_tens1 = tf.placeholder(tf.float32, name='vgg_perf1')
perf_vgg_tens2 = tf.placeholder(tf.float32, name='vgg_perf2')
perf_vgg_tens3 = tf.placeholder(tf.float32, name='vgg_perf3')
perf_vgg_tens4 = tf.placeholder(tf.float32, name='vgg_perf4')
perf_vgg_tens5 = tf.placeholder(tf.float32, name='vgg_perf5')
perf_vgg_tens6 = tf.placeholder(tf.float32, name='vgg_perf6')
angio_vgg_tens0 = tf.placeholder(tf.float32, name='vgg_angio0')
angio_vgg_tens1 = tf.placeholder(tf.float32, name='vgg_angio1')
angio_vgg_tens2 = tf.placeholder(tf.float32, name='vgg_angio2')
angio_vgg_tens3 = tf.placeholder(tf.float32, name='vgg_angio3')
angio_vgg_tens4 = tf.placeholder(tf.float32, name='vgg_angio4')
angio_vgg_tens5 = tf.placeholder(tf.float32, name='vgg_angio5')
angio_vgg_tens6 = tf.placeholder(tf.float32, name='vgg_angio6')
perf_huber_loss_tens = tf.placeholder(tf.float32, name='huber_perf')
angio_huber_loss_tens = tf.placeholder(tf.float32, name='huber_angio')
perf_huber_tens0 = tf.placeholder(tf.float32, name='huber_perf0')
perf_huber_tens1 = tf.placeholder(tf.float32, name='huber_perf1')
perf_huber_tens2 = tf.placeholder(tf.float32, name='huber_perf2')
perf_huber_tens3 = tf.placeholder(tf.float32, name='huber_perf3')
perf_huber_tens4 = tf.placeholder(tf.float32, name='huber_perf4')
perf_huber_tens5 = tf.placeholder(tf.float32, name='huber_perf5')
perf_huber_tens6 = tf.placeholder(tf.float32, name='huber_perf6')
angio_huber_tens0 = tf.placeholder(tf.float32, name='huber_angio0')
angio_huber_tens1 = tf.placeholder(tf.float32, name='huber_angio1')
angio_huber_tens2 = tf.placeholder(tf.float32, name='huber_angio2')
angio_huber_tens3 = tf.placeholder(tf.float32, name='huber_angio3')
angio_huber_tens4 = tf.placeholder(tf.float32, name='huber_angio4')
angio_huber_tens5 = tf.placeholder(tf.float32, name='huber_angio5')
angio_huber_tens6 = tf.placeholder(tf.float32, name='huber_angio6')
# ===================================================================================
densenet = _densenet()
[y, _] = densenet.densenet(img_row1=img_row1,
img_row2=img_row2,
img_row3=img_row3,
img_row4=img_row4,
img_row5=img_row5,
img_row6=img_row6,
img_row7=img_row7,
img_row8=img_row8,
input_dim=input_dim,
is_training=is_training)
vgg = vgg_feature_maker(test=1)
feature_type = 'huber'
vgg_y0 = vgg.feed_img(y[:, :, :, :, 0], feature_type=feature_type).copy()
vgg_y1 = vgg.feed_img(y[:, :, :, :, 1], feature_type=feature_type).copy()
vgg_y2 = vgg.feed_img(y[:, :, :, :, 2], feature_type=feature_type).copy()
vgg_y3 = vgg.feed_img(y[:, :, :, :, 3], feature_type=feature_type).copy()
vgg_y4 = vgg.feed_img(y[:, :, :, :, 4], feature_type=feature_type).copy()
vgg_y5 = vgg.feed_img(y[:, :, :, :, 5], feature_type=feature_type).copy()
vgg_y6 = vgg.feed_img(y[:, :, :, :, 6], feature_type=feature_type).copy()
vgg_y7 = vgg.feed_img(y[:, :, :, :, 7], feature_type=feature_type)
vgg_y8 = vgg.feed_img(y[:, :, :, :, 8], feature_type=feature_type)
vgg_y9 = vgg.feed_img(y[:, :, :, :, 9], feature_type=feature_type)
vgg_y10 = vgg.feed_img(y[:, :, :, :, 10], feature_type=feature_type)
vgg_y11 = vgg.feed_img(y[:, :, :, :, 11], feature_type=feature_type)
vgg_y12 = vgg.feed_img(y[:, :, :, :, 12], feature_type=feature_type)
vgg_y13 = vgg.feed_img(y[:, :, :, :, 13], feature_type=feature_type)
vgg_label0 = vgg.feed_img(label1[:, :, :, :, 0],
feature_type=feature_type).copy()
vgg_label1 = vgg.feed_img(label2[:, :, :, :, 0],
feature_type=feature_type).copy()
vgg_label2 = vgg.feed_img(label3[:, :, :, :, 0],
feature_type=feature_type).copy()
vgg_label3 = vgg.feed_img(label4[:, :, :, :, 0],
feature_type=feature_type).copy()
vgg_label4 = vgg.feed_img(label5[:, :, :, :, 0],
feature_type=feature_type).copy()
vgg_label5 = vgg.feed_img(label6[:, :, :, :, 0],
feature_type=feature_type).copy()
vgg_label6 = vgg.feed_img(label7[:, :, :, :, 0],
feature_type=feature_type).copy()
vgg_label7 = vgg.feed_img(label8[:, :, :, :, 0], feature_type=feature_type)
vgg_label8 = vgg.feed_img(label9[:, :, :, :, 0], feature_type=feature_type)
vgg_label9 = vgg.feed_img(label10[:, :, :, :, 0],
feature_type=feature_type)
vgg_label10 = vgg.feed_img(label11[:, :, :, :, 0],
feature_type=feature_type)
vgg_label11 = vgg.feed_img(label12[:, :, :, :, 0],
feature_type=feature_type)
vgg_label12 = vgg.feed_img(label13[:, :, :, :, 0],
feature_type=feature_type)
vgg_label13 = vgg.feed_img(label14[:, :, :, :, 0],
feature_type=feature_type)
all_loss = tf.placeholder(tf.float32, name='loss')
# is_training = tf.placeholder(tf.bool, name='is_training')
# input_dim = tf.placeholder(tf.int32, name='input_dim')
# ave_huber = tf.placeholder(tf.float32, name='huber')
labels = []
labels.append(label1)
labels.append(label2)
labels.append(label3)
labels.append(label4)
labels.append(label5)
labels.append(label6)
labels.append(label7)
labels.append(label8)
labels.append(label9)
labels.append(label10)
labels.append(label11)
labels.append(label12)
labels.append(label13)
labels.append(label14)
logits = []
logits.append(y[:, :, :, :, 0, np.newaxis])
logits.append(y[:, :, :, :, 1, np.newaxis])
logits.append(y[:, :, :, :, 2, np.newaxis])
logits.append(y[:, :, :, :, 3, np.newaxis])
logits.append(y[:, :, :, :, 4, np.newaxis])
logits.append(y[:, :, :, :, 5, np.newaxis])
logits.append(y[:, :, :, :, 6, np.newaxis])
logits.append(y[:, :, :, :, 7, np.newaxis])
logits.append(y[:, :, :, :, 8, np.newaxis])
logits.append(y[:, :, :, :, 9, np.newaxis])
logits.append(y[:, :, :, :, 10, np.newaxis])
logits.append(y[:, :, :, :, 11, np.newaxis])
logits.append(y[:, :, :, :, 12, np.newaxis])
logits.append(y[:, :, :, :, 13, np.newaxis])
loss_instance = _loss_func()
vgg_in_feature = []
vgg_in_feature.append(vgg_y0)
vgg_in_feature.append(vgg_y1)
vgg_in_feature.append(vgg_y2)
vgg_in_feature.append(vgg_y3)
vgg_in_feature.append(vgg_y4)
vgg_in_feature.append(vgg_y5)
vgg_in_feature.append(vgg_y6)
vgg_in_feature.append(vgg_y7)
vgg_in_feature.append(vgg_y8)
vgg_in_feature.append(vgg_y9)
vgg_in_feature.append(vgg_y10)
vgg_in_feature.append(vgg_y11)
vgg_in_feature.append(vgg_y12)
vgg_in_feature.append(vgg_y13)
vgg_label_feature = []
vgg_label_feature.append(vgg_label0)
vgg_label_feature.append(vgg_label1)
vgg_label_feature.append(vgg_label2)
vgg_label_feature.append(vgg_label3)
vgg_label_feature.append(vgg_label4)
vgg_label_feature.append(vgg_label5)
vgg_label_feature.append(vgg_label6)
vgg_label_feature.append(vgg_label7)
vgg_label_feature.append(vgg_label8)
vgg_label_feature.append(vgg_label9)
vgg_label_feature.append(vgg_label10)
vgg_label_feature.append(vgg_label11)
vgg_label_feature.append(vgg_label12)
vgg_label_feature.append(vgg_label13)
with tf.name_scope('Loss'):
loss_dic = loss_instance.loss_selector(
'content_vgg_pairwise_loss_huber',
labels=vgg_label_feature,
logits=vgg_in_feature,
vgg=vgg,
h_labels=labels,
h_logits=logits)
cost = tf.reduce_mean(loss_dic["loss"], name="cost")
# cost_angio = tf.reduce_mean(loss_dic["angio_SSIM"], name="angio_SSIM")
# cost_perf = tf.reduce_mean(loss_dic["perf_SSIM"], name="perf_SSIM")
# ========================================================================
# ave_loss = tf.placeholder(tf.float32, name='loss')
# ave_loss_perf = tf.placeholder(tf.float32, name='loss_perf')
# ave_loss_angio = tf.placeholder(tf.float32, name='loss_angio')
#
# average_gradient_perf = tf.placeholder(tf.float32, name='grad_ave_perf')
# average_gradient_angio = tf.placeholder(tf.float32, name='grad_ave_angio')
#
# ave_huber = tf.placeholder(tf.float32, name='huber')
# restore the model
sess = tf.Session()
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(chckpnt_dir)
saver.restore(sess, ckpt.model_checkpoint_path)
copyfile('./test_synthesize_ssim_perf_angio.py',
result_path + '/test_synthesize_ssim_perf_angio.py')
_image_class = image_class(train_data,
bunch_of_images_no=1,
is_training=1,
patch_window=patch_window,
sample_no_per_bunch=1,
label_patch_size=label_patchs_size,
validation_total_sample=0)
learning_rate = 1E-5
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(extra_update_ops):
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
# init = tf.global_variables_initializer()
dic_perf0 = []
dic_perf1 = []
dic_perf2 = []
dic_perf3 = []
dic_perf4 = []
dic_perf5 = []
dic_perf6 = []
dic_angio0 = []
dic_angio1 = []
dic_angio2 = []
dic_angio3 = []
dic_angio4 = []
dic_angio5 = []
dic_angio6 = []
loss = 0
for img_indx in range(len(test_set)):
crush, noncrush, perf, angio, mri, segmentation_, spacing, direction, origin = _image_class.read_image_for_test(
test_set=test_set,
img_indx=img_indx,
input_size=in_dim,
final_layer=final_layer)
[out] = sess.run(
[y],
feed_dict={
img_row1: np.expand_dims(np.expand_dims(crush[0], 0), -1),
img_row2: np.expand_dims(np.expand_dims(noncrush[1], 0), -1),
img_row3: np.expand_dims(np.expand_dims(crush[2], 0), -1),
img_row4: np.expand_dims(np.expand_dims(noncrush[3], 0), -1),
img_row5: np.expand_dims(np.expand_dims(crush[4], 0), -1),
img_row6: np.expand_dims(np.expand_dims(noncrush[5], 0), -1),
img_row7: np.expand_dims(np.expand_dims(crush[6], 0), -1),
img_row8: np.expand_dims(np.expand_dims(noncrush[7], 0), -1),
mri_ph: np.expand_dims(np.expand_dims(mri, 0), -1),
label1: np.expand_dims(np.expand_dims(perf[0], 0), -1),
label2: np.expand_dims(np.expand_dims(perf[1], 0), -1),
label3: np.expand_dims(np.expand_dims(perf[2], 0), -1),
label4: np.expand_dims(np.expand_dims(perf[3], 0), -1),
label5: np.expand_dims(np.expand_dims(perf[4], 0), -1),
label6: np.expand_dims(np.expand_dims(perf[5], 0), -1),
label7: np.expand_dims(np.expand_dims(perf[6], 0), -1),
label8: np.expand_dims(np.expand_dims(angio[0], 0), -1),
label9: np.expand_dims(np.expand_dims(angio[1], 0), -1),
label10: np.expand_dims(np.expand_dims(angio[2], 0), -1),
label11: np.expand_dims(np.expand_dims(angio[3], 0), -1),
label12: np.expand_dims(np.expand_dims(angio[4], 0), -1),
label13: np.expand_dims(np.expand_dims(angio[5], 0), -1),
label14: np.expand_dims(np.expand_dims(angio[6], 0), -1),
is_training: False,
input_dim: patch_window,
all_loss: -1.,
angio_vgg_loss_tens: -1, # vgg angio
perf_vgg_loss_tens: -1,
perf_vgg_tens0: -1,
perf_vgg_tens1: -1,
perf_vgg_tens2: -1,
perf_vgg_tens3: -1,
perf_vgg_tens4: -1,
perf_vgg_tens5: -1,
perf_vgg_tens6: -1,
angio_vgg_tens0: -1,
angio_vgg_tens1: -1,
angio_vgg_tens2: -1,
angio_vgg_tens3: -1,
angio_vgg_tens4: -1,
angio_vgg_tens5: -1,
angio_vgg_tens6: -1,
perf_huber_loss_tens: -1,
angio_huber_loss_tens: -1,
perf_huber_tens0: -1,
perf_huber_tens1: -1,
perf_huber_tens2: -1,
perf_huber_tens3: -1,
perf_huber_tens4: -1,
perf_huber_tens5: -1,
perf_huber_tens6: -1,
angio_huber_tens0: -1,
angio_huber_tens1: -1,
angio_huber_tens2: -1,
angio_huber_tens3: -1,
angio_huber_tens4: -1,
angio_huber_tens5: -1,
angio_huber_tens6: -1,
})
for i in range(np.shape(out)[-1]):
image = out[0, :, :, :, i]
sitk_image = sitk.GetImageFromArray(image)
res_dir = test_set[img_indx][0][0].split('/')[-2]
if i == 0:
os.mkdir(parent_path + 'results/' + res_dir)
if i < 7:
nm = 'perf'
else:
nm = 'angi'
sitk_image.SetDirection(direction=direction)
sitk_image.SetOrigin(origin=origin)
sitk_image.SetSpacing(spacing=spacing)
sitk.WriteImage(
sitk_image, parent_path + 'results/' + res_dir + '/' + nm +
'_' + str(i % 7) + '.mha')
print(parent_path + 'results/' + res_dir + '/' + nm + '_' +
str(i % 7) + '.mha done!')
for i in range(7):
if i == 0:
os.mkdir(parent_path + 'results/' + res_dir + '/GT/')
sitk_angio = sitk.GetImageFromArray(angio[i])
sitk_angio.SetDirection(direction=direction)
sitk_angio.SetOrigin(origin=origin)
sitk_angio.SetSpacing(spacing=spacing)
sitk.WriteImage(
sitk_angio, parent_path + 'results/' + res_dir + '/GT/angio_' +
str(i) + '.mha')
sitk_perf = sitk.GetImageFromArray(perf[i])
sitk_perf.SetDirection(direction=direction)
sitk_perf.SetOrigin(origin=origin)
sitk_perf.SetSpacing(spacing=spacing)
sitk.WriteImage(
sitk_perf, parent_path + 'results/' + res_dir + '/GT/perf_' +
str(i) + '.mha')
a = 1
dic_perf0.append(
anly.analysis(out[0, :, :, :, 0], perf[i], 0, max_perf))
dic_perf1.append(
anly.analysis(out[0, :, :, :, 1], perf[i], 0, max_perf))
dic_perf2.append(
anly.analysis(out[0, :, :, :, 2], perf[i], 0, max_perf))
dic_perf3.append(
anly.analysis(out[0, :, :, :, 3], perf[i], 0, max_perf))
dic_perf4.append(
anly.analysis(out[0, :, :, :, 4], perf[i], 0, max_perf))
dic_perf5.append(
anly.analysis(out[0, :, :, :, 5], perf[i], 0, max_perf))
dic_perf6.append(
anly.analysis(out[0, :, :, :, 6], perf[i], 0, max_perf))
dic_angio0.append(
anly.analysis(out[0, :, :, :, 7], angio[i], 0, max_angio))
dic_angio1.append(
anly.analysis(out[0, :, :, :, 8], angio[i], 0, max_angio))
dic_angio2.append(
anly.analysis(out[0, :, :, :, 9], angio[i], 0, max_angio))
dic_angio3.append(
anly.analysis(out[0, :, :, :, 10], angio[i], 0, max_angio))
dic_angio4.append(
anly.analysis(out[0, :, :, :, 11], angio[i], 0, max_angio))
dic_angio5.append(
anly.analysis(out[0, :, :, :, 12], angio[i], 0, max_angio))
dic_angio6.append(
anly.analysis(out[0, :, :, :, 13], angio[i], 0, max_angio))
if img_indx == 0:
headers = dic_perf0[0].keys()
dics = [
dic_perf0, dic_perf1, dic_perf2, dic_perf3, dic_perf4, dic_perf5,
dic_perf6, dic_angio0, dic_angio1, dic_angio2, dic_angio3,
dic_angio4, dic_angio5, dic_angio6
]
save_in_xlsx(parent_path, headers, dics=dics)
# plt.imshow(out[0, int(gt_cube_size / 2), :, :, 0])
# plt.figure()
# loss += loss_train1
# print('Loss_train: ', loss_train1)
print('Total loss: ', loss / len(test_set))