def accuracy(predictions, labels, alphabet):
predictions = predictions.softmax().topk(axis=2).asnumpy()
zipped = zip(decode(predictions, alphabet),
decode(labels.asnumpy(), alphabet))
n_correct = 0
for pred, target in zipped:
if pred == target:
n_correct += 1
return n_correct
def accuracy_batch(self, predictions, labels, phase):
n_correct = 0
edit_dis = 0.0
for gpu_prediction, gpu_label in zip(predictions, labels):
gpu_prediction = gpu_prediction.softmax().asnumpy()
zipped = zip(decode(gpu_prediction, self.alphabet),
decode(gpu_label.asnumpy(), self.alphabet))
logged = False
for (pred, pred_conf), (target, _) in zipped:
if self.tensorboard_enable and not logged:
self.writer.add_text(tag='{}/pred'.format(phase),
text='pred: {} -- gt:{}'.format(
pred, target),
global_step=self.global_step)
logged = True
edit_dis += Levenshtein.distance(pred, target)
if pred == target:
n_correct += 1
return {'n_correct': n_correct, 'edit_dis': edit_dis}
def gey_summary():
args = ARGS()
text = request.form['text']
lang = detect(text)
print('Language: ', lang)
if lang == 'ja':
model_dir = model_dir_ja
extractor = extractor_ja
abstractor = abstractor_ja
else:
model_dir = model_dir_en
extractor = extractor_en
abstractor = abstractor_en
setattr(args, 'model_dir', model_dir)
setattr(args, 'batch', 1)
setattr(args, 'beam', beam_size)
setattr(args, 'div', 1.0)
setattr(args, 'max_dec_word', 30)
setattr(args, 'cuda', cuda)
setattr(args, 'extractor', extractor)
setattr(args, 'abstractor', abstractor)
#text = request.form['text']
#print(text)
setattr(args, 'text', text)
text, result, source_text = decode(args, True)
print(source_text)
#print(type(result))
if lang == 'ja':
result = result.replace(' ', '')
else:
result = result.split('\n\n')
for i, r in enumerate(result):
r = r[0].upper() + r[1:]
result[i] = r
result = '\n\n'.join(result)
#print(result)
with open('/mnt/binhna/summary/log.txt', 'a') as f:
f.write(
f"\n\n======================={datetime.now().strftime('%Y-%m-%d %H:%M:%S')}=======================\n"
)
f.write(text)
f.write(
"\n=============================================================================================\n"
)
f.write(result)
return jsonify({'summary': result, 'highlight': source_text})