def main():
import tensorflow as tf
from tensorflow.keras import backend as K
from futils import segmentor as v_seg
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # dynamically grow the memory used on the GPU
sess = tf.Session(config=config)
K.set_session(
sess) # set this TensorFlow session as the default session for Keras
task = 'lobe'
str_name = "1600642845_85_lrlb0.0001lrvs1e-05mtscale0netnol-nnl-novpm0.0nldLUNA16ao0ds0pps100lbnb17vsnb50nlnb400ptsz144ptzsz96"
# mypath = Mypath(task=task, current_time=str_name) # set task=vessel to predict the lobe masks of SSc
model_name = '/data/jjia/new/models/' + task + '/' + str_name + '_valid.hdf5'
# model_name = mypath.model_fpath_best_whole("valid", str_name)
tr_sp, tr_z_sp = 1.4, 2.5
tr_sz, tr_z_sz = None, None
pt_sz, pt_z_sz = 144, 96
print('patch_sz', pt_sz, 'patch_z_size', pt_z_sz)
stride = 0.25
segment = v_seg.v_segmentor(batch_size=1,
model=model_name,
ptch_sz=pt_sz,
ptch_z_sz=pt_z_sz,
trgt_sz=tr_sz,
trgt_z_sz=tr_z_sz,
trgt_space_list=[tr_z_sp, tr_sp, tr_sp],
task=task,
attention=False)
print('stride is', stride)
sub_dirs = ["COPE", "CONP", "NCPE"]
for sub_dir in sub_dirs:
write_preds_to_disk(
segment=segment,
data_dir="/data/jjia/new/COVID/ori/" + sub_dir + "/001",
preds_dir="/data/jjia/new/COVID/results/" + sub_dir + "/001",
number=2,
stride=stride,
workers=1,
qsize=1)
labels = [1]
fissure, lung = False, True
if fissure:
gntFissure(mypath.pred_path("valid",
sub_dir=sub_dir,
cntd_pts=biggest_5_lobe),
radiusValue=radiusValue,
workers=10)
else:
segment = v_seg.v_segmentor(
batch_size=1,
model=model_name,
ptch_sz=pt_sz,
ptch_z_sz=pt_z_sz,
trgt_sz=tr_sz,
trgt_z_sz=tr_z_sz,
trgt_space_list=[tr_z_sp, tr_sp, tr_sp],
task=task,
attention=False)
print('stride is', stride)
workers = 5 if sub_dir == "GLUCOLD" else 5
write_preds_to_disk(
segment=segment,
data_dir=mypath.ori_ct_path("valid", sub_dir=sub_dir),
preds_dir=mypath.pred_path("valid", sub_dir=sub_dir),
number=None,
stride=stride,
workers=workers,
def main():
mypath = Mypath("lobe")
label = [0, 4, 5, 6, 7, 8]
net = sr_model(lr=0.0001)
model_figure_fpath = mypath.model_figure_path() + '/sr.png'
plot_model(net, show_shapes=True, to_file=model_figure_fpath)
print('successfully plot model structure at: ', model_figure_fpath)
# save model architecture and config
model_json = net.to_json()
with open(mypath.json_fpath(), "w") as json_file:
json_file.write(model_json)
print('successfully write new json file of task lobe',
mypath.json_fpath())
x_dir = os.path.join(mypath.train_dir(), "gdth_ct", mypath.sub_dir())
x_ts_dir = os.path.join(mypath.valid_dir(), "gdth_ct", mypath.sub_dir())
tr_it = Sr_data_itr(x_dir=x_dir,
x_ext=".nrrd",
nb=18,
data_argum=1,
pps=1000)
# enqueuer_train = GeneratorEnqueuer(tr_it.generator(), use_multiprocessing=True)
# train_datas = enqueuer_train.get()
# enqueuer_train.start()
train_datas = tr_it.generator()
train_csvlogger = callbacks.CSVLogger(mypath.train_log_fpath(),
separator=',',
append=True)
saver_train = callbacks.ModelCheckpoint(
filepath=mypath.model_fpath_best_train(),
save_best_only=True,
monitor='loss',
save_weights_only=True,
save_freq=1)
steps = 100
for i in range(steps):
# x, y = next(train_datas)
# x, y = x[np.newaxis, ...], y[np.newaxis, ...]
net.fit(train_datas,
steps_per_epoch=18000,
use_multiprocessing=True,
callbacks=[saver_train, train_csvlogger])
# period_valid = 50000
# if i % (period_valid) == (period_valid-1): # one epoch for lobe segmentation, 20 epochs for vessel segmentation
# # save predicted results and compute the dices
for phase in ['train', 'valid']:
segment = v_seg.v_segmentor(batch_size=1,
model=mypath.model_fpath_best_train(),
ptch_sz=144,
ptch_z_sz=96,
trgt_space_list=[None, None, None],
task='lobe',
sr=True)
write_preds_to_disk(segment=segment,
data_dir=mypath.ori_ct_path(phase),
preds_dir=mypath.pred_path(phase),
number=1,
stride=0.25) # set stride 0.8 to save time
write_dices_to_csv(step_nb=i,
labels=label,
gdth_path=mypath.gdth_path(phase),
pred_path=mypath.pred_path(phase),
csv_file=mypath.dices_fpath(phase))
save_model_best(dice_file=mypath.dices_fpath(phase),
phase=phase,
segment=segment,
model_fpath=mypath.model_fpath_best_valid())
print('step number',
i,
'lr for super resolutin is',
K.eval(net.optimizer.lr),
file=sys.stderr)
try:
ptch_sz = int(model_number[-10:-7])
except:
ptch_sz = int(model_number[-9:-7])
print('ptch_sz, {}, ptch_z_sz, {}'.format(ptch_sz, ptch_z_sz))
if fixed_space:
trgt_space = [0.3, 0.5, 0.5]
elif fixed_size:
trgt_size = [128, 256, 256]
else:
trgt_size = None
#LOAD THE MODEL
segment = v_seg.v_segmentor(batch_size=1,
model='models/' + task + '/' + model_number +
'MODEL.hdf5',
patching=True)
print('finished first model loading')
model = segment.v
plot_model(model, show_shapes=True, to_file='lobemodel.png')
#
# K.set_learning_phase(0)
# task = 'lobe'
# model_number = '1583105196.4919333_0.00010a_o_0.5ds2dr1bn1fs16ptsz144ptzsz80'
#
# #LOAD THE MODEL
# segment_ = v_seg.v_segmentor(batch_size=1, model='models/' + task + '/' + model_number+'MODEL.hdf5', patching=False)
# print('finished first model loading')
# model_ = segment_.v