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data_preprocessing_7w.py

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+
+import json
+import os
+import argparse
+import pdb
+from nltk.tokenize import word_tokenize
+import numpy as np
+
+def main(params):
+	train = []
+	test = []
+	imdir='v7w_%s.jpg'
+	print 'Loading annotations and questions...'
+	data = json.load(open('dataset_v7w_%s.json' %(params['data_set']), 'r'))["images"]
+
+	train_txt = open("train_ques_ans.txt", "w")
+	test_txt = open("test_ques_ans.txt", "w")
+
+	for image in data:
+		# print image.keys()
+		for QA in image['qa_pairs']:
+			correct_ans = QA['answer']
+			question_id = QA['qa_id']
+			image_path = imdir%(QA["image_id"])
+			question = QA['question']
+			# add correct answer
+			if image['split'] == 'test':
+				test_txt.write(str(question) + "\n")
+				test_txt.write(str(correct_ans) + "\n")
+				test.append({'ques_id': question_id, 'img_path': image_path, 'question': question, 'MC_ans': correct_ans, 'ans': 1})
+			else:
+				train_txt.write(str(question) + "\n")
+				train_txt.write(str(correct_ans) + "\n")
+				train.append({'ques_id': question_id, 'img_path': image_path, 'question': question, 'MC_ans': correct_ans, 'ans': 1})
+			mc_ans = QA['multiple_choices']
+			assert len(mc_ans) == 3
+			# add wrong answers
+			for wrong_ans in mc_ans:
+				if image['split'] == 'test':
+					test_txt.write(str(wrong_ans) + "\n")
+					test.append({'ques_id': question_id, 'img_path': image_path, 'question': question, 'MC_ans': wrong_ans, 'ans': 0})
+				else:
+					train_txt.write(str(wrong_ans) + "\n")
+					train.append({'ques_id': question_id, 'img_path': image_path, 'question': question, 'MC_ans': wrong_ans, 'ans': 0})
+
+	train_txt.close()
+	test_txt.close()
+	json.dump(train, open('vqa_raw_train.json', 'w'))
+	json.dump(test, open('vqa_raw_test.json', 'w'))
+
+if __name__ == "__main__":
+	parser = argparse.ArgumentParser()
+	parser.add_argument('--data_set', default = 'telling',help = 'which data set, telling or pointing')
+	args = parser.parse_args()
+	params = vars(args) # convert to ordinary dict
+	main(params)
+
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