def main():
parser = argparse.ArgumentParser()
parser.add_argument('--make_data', action='store_true')
args = parser.parse_args()
if args.make_data:
make_data(get_base_param())
prepare(get_base_param())
#save_param
common_io.dump_to_file(get_base_param()['param_save_path'],get_base_param())
print 'have a good day'
data_set = {}
data_set['image_text.train'] = label_list1
data_set['image_text.val'] = label_list2
vocab_list = []
for vac in label_list1+label_list2:
tmp = vac.split('\t')
if len(tmp)>1:
vocab_list.append(tmp[1])
else:
vocab_list.append("")
misc = statistic_vocabulary.preProBuildWordVocab(vocab_list)
common_io.dump_to_file(vocab_out , misc)
#misc = common_io.load_from_file(vocab_out)
wordtoix = misc['wordtoix']
print "vocab size"
print len(wordtoix)
for file_name,label_list in data_set.items():
print 'Starting %s' % file_name
ix_label_out_file = nlpcaffe_data_out_dir + file_name
ix_label_out = []
for i in xrange(0,len(label_list)):
labels = label_list[i]
def main():
label_list = common_io.read_txt_lines( text_file_in ,remove_space = True)
misc = preProBuildWordVocab(label_list, word_count_threshold)
common_io.dump_to_file(vocab_out , misc)
