positive_batch_size = (1 - int(ALL_NEGATIVE)) * (
int(BATCH_SIZE / 2 / aug_proportion) - 1
) + 1 #if augmentatiion implemented then set batch size to 1 or half of BATCH_SIZE
#negative_batch_size = int(positive_batch_size * np_proportion)
for pbi in tqdm(range(0, positive_train_num, positive_batch_size)):
#if AUGMENTATION:
# print("training process:%d/%d" %(pbi, positive_train_num))
posbatchend = min(pbi + positive_batch_size,
positive_train_num)
for pti in range(pbi, posbatchend):
data_index = positive_train_indices[pti]
pfile = tpfiles[data_index]
#pfile = 'luna_cubes_56_overbound/subset5/npy/1.3.6.1.4.1.14519.5.2.1.6279.6001.112740418331256326754121315800_34_ob_annotations.npy'
if pfile.split('/')[0].find("luna") >= 0:
patient_uid, nodule_diameter = mt.get_luna_annotation_informations(
pfile,
"luna_cubes_56_overbound/luna_annotations.csv")
elif pfile.split('/')[0].find("tianchi") >= 0:
patient_uid, nodule_diameter = mt.get_luna_annotation_informations(
pfile,
"tianchi_cubes_56_overbound/tianchi_annotations.csv"
)
else:
patient_uid = mt.get_volume_informations(pfile)[0]
nodule_diameter = -1
positive_data = np.load(pfile)
if "positive_batch" not in dir():
positive_batch = extract_volumes(
positive_data, nodule_diameter=nodule_diameter)
#positive_batch = extract_volumes(positive_data, nodule_diameter=0)
else:
negative_train_accuracy = 0.0
import_batch_size = 900
for pbi in range(0, train_num, import_batch_size):
print("training process:%d/%d" % (pbi, train_num))
for pti in range(pbi, min(pbi + import_batch_size, train_num)):
data_index = train_indices[pti]
if data_index < num_positive:
pfile = pfiles[data_index]
isnodule = True
else:
pfile = nfiles[data_index - num_positive]
isnodule = False
data_volume = np.load(pfile)
if isnodule:
if pfile.split('/')[0].find("luna") >= 0:
patient_uid, nodule_diameter = mt.get_luna_annotation_informations(
pfile, annotation)
elif pfile.split('/')[0].find("tianchi") >= 0:
patient_uid, nodule_diameter = mt.get_luna_annotation_informations(
pfile, annotation2)
else:
patient_uid = mt.get_volume_informations(pfile)[0]
nodule_diameter = 0
data_volume = da.extract_volumes(
data_volume,
np.int_([REGION_SIZE, REGION_SIZE, REGION_SIZE]),
nodule_diameter=nodule_diameter,
scale_augment=AUGMENTATION,
translation_augment=AUGMENTATION,
rotation_augment=AUGMENTATION)
else:
data_volume = mt.local_crop(
