def predict(self, image=None, text=None):
from data_processing import img_feature_get, text_feature_get
if torch.cuda.is_available():
self.text_model.cuda()
self.img_model.cuda()
else:
self.text_model.cpu()
self.img_model.cpu()
img_encode, img_decode1, img_decode2 = ([], [], [])
text_encode, text_decode1, text_decode2 = ([], [], [])
if image:
a = img_feature_get.get_img_feature([image])[0]
if torch.cuda.is_available():
a = a.cuda()
img_encode, img_decode1, img_decode2 = self.img_model(a)
if text:
a = text_feature_get.get_text_feature([text])[0]
if torch.cuda.is_available():
a = a.cuda()
text_encode, text_decode1, text_decode2 = self.text_model(a)
if image and text:
return img_encode.detach().cpu(), text_encode.detach().cpu()
if image:
return img_encode.detach().cpu()
if text:
return text_encode.detach().cpu()
def united_search(self, get_img=True):
for label in self.img_labels:
label.close()
for label in self.text_labels:
label.close()
global img_data, text_data, _img_data, _text_data
if get_img and self.text_file:
f = open(self.text_file, 'r')
text = f.read()
result = self.model.search_top3(
mode=2,
search_data=img_data,
text=text_feature_get.get_text_feature([text])[0])
for i in range(3):
self.img_labels[i].setPixmap(
QtGui.QPixmap(_img_data[result[i]]))
self.img_labels[i].show()
elif not get_img and self.img_file:
result = self.model.search_top3(
mode=1,
search_data=text_data,
img=img_feature_get.get_img_feature([self.img_file],
mode=3)[0])
for i in range(3):
self.text_labels[i].setText(_text_data[result[i]])
self.text_labels[i].show()
def run(self):
global _img_data, img_data
img_data = img_feature_get.get_img_feature(files=_img_data, mode=1)
global n
n -= 1
def run(self):
global _img_data, img_data, y
img_data = img_feature_get.get_img_feature(files=_img_data[:5000],
mode=y)
global n
n -= 1
model = ECAE.ECAE(text_size=len(text_data[0]),
img_size=len(img_data[0]),
model_name='resnet101')
model.load()
while True:
mode = int(
input(
'search mode:1.img2text 2.text2img 3.img_text2text 4.img_text2img 5.exit\n'
))
if mode == 5:
break
data = input("input data:\n")
if mode == 1:
result = model.search_top3(mode=mode,
search_data=text_data,
img=img_feature_get.get_img_feature(
[data], mode=y)[0])
elif mode == 2:
result = model.search_top3(mode=mode,
search_data=img_data,
text=text_feature_get.get_text_feature(
[data])[0])
elif mode == 3:
_data = input()
result = model.search_top3(
mode=mode,
search_data=text_data,
img=img_feature_get.get_img_feature([data], mode=y)[0],
text=text_feature_get.get_text_feature([_data])[0])
else:
_data = input()
result = model.search_top3(