def main():
parser = argparse.ArgumentParser()
parser.add_argument('--num_lstm_layers', type=int, default=2,
help='num_lstm_layers')
parser.add_argument('--fc7_feature_length', type=int, default=4096,
help='fc7_feature_length')
parser.add_argument('--rnn_size', type=int, default=512,
help='rnn_size')
parser.add_argument('--embedding_size', type=int, default=512,
help='embedding_size'),
parser.add_argument('--word_emb_dropout', type=float, default=0.5,
help='word_emb_dropout')
parser.add_argument('--image_dropout', type=float, default=0.5,
help='image_dropout')
parser.add_argument('--data_dir', type=str, default='Data',
help='Data directory')
parser.add_argument('--batch_size', type=int, default=200,
help='Batch Size')
parser.add_argument('--learning_rate', type=float, default=0.001,
help='Batch Size')
parser.add_argument('--epochs', type=int, default=200,
help='Expochs')
parser.add_argument('--debug', type=bool, default=False,
help='Debug')
parser.add_argument('--resume_model', type=str, default=None,
help='Trained Model Path')
args = parser.parse_args()
print "Reading QA DATA"
qa_data = data_loader.load_questions_answers(args)
print "Reading fc7 features"
fc7_features, image_id_list = data_loader.load_fc7_features(args.data_dir, 'train')
print "FC7 features", fc7_features.shape
print "image_id_list", image_id_list.shape
image_id_map = {}
for i in xrange(len(image_id_list)):
image_id_map[ image_id_list[i] ] = i
ans_map = { qa_data['answer_vocab'][ans] : ans for ans in qa_data['answer_vocab']}
model_options = {
'num_lstm_layers' : args.num_lstm_layers,
'rnn_size' : args.rnn_size,
'embedding_size' : args.embedding_size,
'word_emb_dropout' : args.word_emb_dropout,
'image_dropout' : args.image_dropout,
'fc7_feature_length' : args.fc7_feature_length,
'lstm_steps' : qa_data['max_question_length'] + 1,
'q_vocab_size' : len(qa_data['question_vocab']),
'ans_vocab_size' : len(qa_data['answer_vocab'])
}
model = vis_lstm_model.Vis_lstm_model(model_options)
input_tensors, t_loss, t_accuracy, t_p = model.build_model()
train_op = tf.train.AdamOptimizer(args.learning_rate).minimize(t_loss)
sess = tf.InteractiveSession()
tf.initialize_all_variables().run()
saver = tf.train.Saver()
if args.resume_model:
saver.restore(sess, args.resume_model)
for i in xrange(args.epochs):
batch_no = 0
while (batch_no*args.batch_size) < len(qa_data['training']):
sentence, answer, fc7 = get_training_batch(batch_no, args.batch_size, fc7_features, image_id_map, qa_data, 'train')
_, loss_value, accuracy, pred = sess.run([train_op, t_loss, t_accuracy, t_p],
feed_dict={
input_tensors['fc7']:fc7,
input_tensors['sentence']:sentence,
input_tensors['answer']:answer
}
)
batch_no += 1
if args.debug:
for idx, p in enumerate(pred):
print ans_map[p], ans_map[ np.argmax(answer[idx])]
print "Loss", loss_value, batch_no, i
print "Accuracy", accuracy
print "---------------"
else:
print "Loss", loss_value, batch_no, i
print "Training Accuracy", accuracy
save_path = saver.save(sess, "Data/Models/model{}.ckpt".format(i))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--num_lstm_layers', type=int, default=2,
help='num_lstm_layers')
parser.add_argument('--fc7_feature_length', type=int, default=4096,
help='fc7_feature_length')
parser.add_argument('--rnn_size', type=int, default=512,
help='rnn_size')
parser.add_argument('--embedding_size', type=int, default=512,
help='embedding_size'),
parser.add_argument('--word_emb_dropout', type=float, default=0.5,
help='word_emb_dropout')
parser.add_argument('--image_dropout', type=float, default=0.5,
help='image_dropout')
parser.add_argument('--data_dir', type=str, default='Data',
help='Data directory')
parser.add_argument('--batch_size', type=int, default=200,
help='Batch Size')
parser.add_argument('--learning_rate', type=float, default=0.001,
help='Batch Size')
parser.add_argument('--epochs', type=int, default=200,
help='Expochs')
parser.add_argument('--debug', type=bool, default=False,
help='Debug')
parser.add_argument('--model_path', type=str, default = 'Data/Models/model21.ckpt',
help='Model Path')
args = parser.parse_args()
print "Reading QA DATA"
qa_data = data_loader.load_questions_answers(args)
print "Reading fc7 features"
fc7_features, image_id_list = data_loader.load_fc7_features(args.data_dir, 'val')
print "FC7 features", fc7_features.shape
print "image_id_list", image_id_list.shape
image_id_map = {}
for i in xrange(len(image_id_list)):
image_id_map[ image_id_list[i] ] = i
ans_map = { qa_data['answer_vocab'][ans] : ans for ans in qa_data['answer_vocab']}
model_options = {
'num_lstm_layers' : args.num_lstm_layers,
'rnn_size' : args.rnn_size,
'embedding_size' : args.embedding_size,
'word_emb_dropout' : args.word_emb_dropout,
'image_dropout' : args.image_dropout,
'fc7_feature_length' : args.fc7_feature_length,
'lstm_steps' : qa_data['max_question_length'] + 1,
'q_vocab_size' : len(qa_data['question_vocab']),
'ans_vocab_size' : len(qa_data['answer_vocab'])
}
model = vis_lstm_model.Vis_lstm_model(model_options)
input_tensors, t_prediction, t_ans_probab = model.build_generator()
sess = tf.InteractiveSession()
saver = tf.train.Saver()
avg_accuracy = 0.0
total = 0
saver.restore(sess, args.model_path)
batch_no = 0
while (batch_no*args.batch_size) < len(qa_data['validation']):
sentence, answer, fc7 = get_batch(batch_no, args.batch_size,
fc7_features, image_id_map, qa_data, 'val')
pred, ans_prob = sess.run([t_prediction, t_ans_probab], feed_dict={
input_tensors['fc7']:fc7,
input_tensors['sentence']:sentence,
})
batch_no += 1
if args.debug:
for idx, p in enumerate(pred):
print ans_map[p], ans_map[ np.argmax(answer[idx])]
correct_predictions = np.equal(pred, np.argmax(answer, 1))
correct_predictions = correct_predictions.astype('float32')
accuracy = correct_predictions.mean()
print "Acc", accuracy
avg_accuracy += accuracy
total += 1
print "Acc", avg_accuracy/total
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--num_lstm_layers',
type=int,
default=2,
help='num_lstm_layers')
parser.add_argument('--fc7_feature_length',
type=int,
default=4096,
help='fc7_feature_length')
parser.add_argument('--rnn_size', type=int, default=128, help='rnn_size')
parser.add_argument('--embedding_size',
type=int,
default=128,
help='embedding_size'),
parser.add_argument('--word_emb_dropout',
type=float,
default=0.5,
help='word_emb_dropout')
parser.add_argument('--image_dropout',
type=float,
default=0.5,
help='image_dropout')
parser.add_argument('--data_dir',
type=str,
default='data',
help='Data directory')
parser.add_argument('--batch_size',
type=int,
default=10,
help='Batch Size')
# parser.add_argument('--learning_rate', type=float, default=0.001,
# help='Batch Size')
# parser.add_argument('--epochs', type=int, default=20,
# help='Expochs')
parser.add_argument('--debug', type=bool, default=False, help='Debug')
parser.add_argument('--model_path',
type=str,
default='19',
help='Model Path')
parser.add_argument('--version',
type=int,
default=2,
help='VQA data version')
parser.add_argument('--model',
type=str,
default="cnn_lstm_model",
help='model to run')
args = parser.parse_args()
print("Reading QA DATA")
qa_data = data_loader.load_data(args.data_dir)
print("Reading fc7 features")
fc7_features, image_id_list = data_loader.load_fc7_features(
args.data_dir, 'val')
print("FC7 features", fc7_features.shape)
print("image_id_list", image_id_list.shape)
image_id_map = {}
for i in range(len(image_id_list)):
image_id_map[image_id_list[i]] = i
ans_map = {
qa_data['topic_vocab'][ans]: ans
for ans in qa_data['topic_vocab']
}
lstm_steps = qa_data[
'max_conversation_length'] if args.model == "lstm_model" else qa_data[
'max_conversation_length'] + 1
model_options = {
'num_lstm_layers': args.num_lstm_layers,
'rnn_size': args.rnn_size,
'embedding_size': args.embedding_size,
'word_emb_dropout': args.word_emb_dropout,
'image_dropout': args.image_dropout,
'fc7_feature_length': args.fc7_feature_length,
'lstm_steps': lstm_steps,
'q_vocab_size': len(qa_data['conversation_vocab']),
'ans_vocab_size': len(qa_data['topic_vocab'])
}
if args.model == "cnn_lstm_model":
model = lstm_model.lstm_model(model_options)
elif args.model == "cnn_model":
model = cnn_model.cnn_model(model_options)
elif args.model == "lstm_model":
model = lstm_model.lstm_model(model_options)
else:
model = None
input_tensors, t_prediction, t_ans_probab = model.build_generator()
sess = tf.InteractiveSession()
saver = tf.train.Saver()
avg_accuracy = 0.0
total = 0
model_path = "data/models/" + args.model + args.model_path + ".ckpt"
saver.restore(sess, model_path)
batch_no = 0
while (batch_no * args.batch_size) < len(qa_data['validation']):
sentence, answer, fc7 = get_batch(batch_no, args.batch_size,
fc7_features, image_id_map, qa_data,
'val')
pred, ans_prob = sess.run([t_prediction, t_ans_probab],
feed_dict={
input_tensors['fc7']: fc7,
input_tensors['sentence']: sentence,
})
batch_no += 1
if args.debug:
for idx, p in enumerate(pred):
print(ans_map[p], ans_map[np.argmax(answer[idx])])
correct_predictions = np.equal(pred, np.argmax(answer, 1))
correct_predictions = correct_predictions.astype('float32')
accuracy = correct_predictions.mean()
print("Acc", accuracy)
avg_accuracy += accuracy
total += 1
print("Acc", avg_accuracy / total)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--num_lstm_layers',
type=int,
default=2,
help='num_lstm_layers')
parser.add_argument('--fc7_feature_length',
type=int,
default=4096,
help='fc7_feature_length')
parser.add_argument('--rnn_size', type=int, default=512, help='rnn_size')
parser.add_argument('--embedding_size',
type=int,
default=512,
help='embedding_size'),
parser.add_argument('--word_emb_dropout',
type=float,
default=0.5,
help='word_emb_dropout')
parser.add_argument('--image_dropout',
type=float,
default=0.5,
help='image_dropout')
parser.add_argument('--data_dir',
type=str,
default='Data',
help='Data directory')
parser.add_argument('--batch_size',
type=int,
default=200,
help='Batch Size')
parser.add_argument('--learning_rate',
type=float,
default=0.001,
help='Batch Size')
parser.add_argument('--epochs', type=int, default=200, help='Expochs')
parser.add_argument('--debug', type=bool, default=False, help='Debug')
parser.add_argument('--resume_model',
type=str,
default=None,
help='Trained Model Path')
parser.add_argument('--version',
type=int,
default=2,
help='VQA data version')
args = parser.parse_args()
# Reading QA DATA
qa_data = data_loader.load_questions_answers(args.version, args.data_dir)
fc7_features, image_id_list = data_loader.load_fc7_features(
args.data_dir, 'train')
#Load features with image data set
image_id_map = {}
for i in xrange(len(image_id_list)):
image_id_map[image_id_list[i]] = i
#Associate answers to
ans_map = {
qa_data['answer_vocab'][ans]: ans
for ans in qa_data['answer_vocab']
}
model_options = {
'num_lstm_layers': args.num_lstm_layers,
'rnn_size': args.rnn_size,
'embedding_size': args.embedding_size,
'word_emb_dropout': args.word_emb_dropout,
'image_dropout': args.image_dropout,
'fc7_feature_length': args.fc7_feature_length,
'lstm_steps': qa_data['max_question_length'] + 1,
'q_vocab_size': len(qa_data['question_vocab']),
'ans_vocab_size': len(qa_data['answer_vocab'])
}
# Use tensorflow's LSTM Cell to train model for image features on question and answer vocab
model = tf.contrib.rnn.BasicLSTMCell(model_options)
input_tensors, t_loss, t_accuracy, t_p = model.build_model()
train_op = tf.train.AdamOptimizer(args.learning_rate).minimize(t_loss)
sess = tf.InteractiveSession()
tf.initialize_all_variables().run()
saver = tf.train.Saver()
if args.resume_model:
saver.restore(sess, args.resume_model)
#for every batch iteration
for i in xrange(args.epochs):
batch_no = 0
# modify tensor's weights
while (batch_no * args.batch_size) < len(qa_data['training']):
sentence, answer, fc7 = get_training_batch(batch_no,
args.batch_size,
fc7_features,
image_id_map, qa_data,
'train')
_, loss_value, accuracy, pred = sess.run(
[train_op, t_loss, t_accuracy, t_p],
feed_dict={
input_tensors['fc7']: fc7,
input_tensors['sentence']: sentence,
input_tensors['answer']: answer
})
# and optimize loss accuracy using Adam Optimizer
batch_no += 1
if args.debug:
for idx, p in enumerate(pred):
print ans_map[p], ans_map[np.argmax(answer[idx])]
print "Loss", loss_value, batch_no, i
print "Accuracy", accuracy
print "---------------"
else:
print "Loss", loss_value, batch_no, i
print "Training Accuracy", accuracy
save_path = saver.save(sess, "Data/Models/model{}.ckpt".format(i))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--num_lstm_layers',
type=int,
default=2,
help='num_lstm_layers')
parser.add_argument('--fc7_feature_length',
type=int,
default=4096,
help='fc7_feature_length')
parser.add_argument('--rnn_size', type=int, default=512, help='rnn_size')
parser.add_argument('--embedding_size',
type=int,
default=512,
help='embedding_size'),
parser.add_argument('--word_emb_dropout',
type=float,
default=0.5,
help='word_emb_dropout')
parser.add_argument('--image_dropout',
type=float,
default=0.5,
help='image_dropout')
parser.add_argument('--data_dir',
type=str,
default='Data',
help='Data directory')
parser.add_argument('--batch_size',
type=int,
default=200,
help='Batch Size')
parser.add_argument('--learning_rate',
type=float,
default=0.001,
help='Batch Size')
parser.add_argument('--epochs', type=int, default=200, help='Expochs')
parser.add_argument('--debug', type=bool, default=False, help='Debug')
parser.add_argument('--model_path',
type=str,
default='Data/Models/model21.ckpt',
help='Model Path')
args = parser.parse_args()
print "Reading QA DATA"
qa_data = data_loader.load_questions_answers(args)
print "Reading fc7 features"
fc7_features, image_id_list = data_loader.load_fc7_features(
args.data_dir, 'val')
print "FC7 features", fc7_features.shape
print "image_id_list", image_id_list.shape
image_id_map = {}
for i in xrange(len(image_id_list)):
image_id_map[image_id_list[i]] = i
ans_map = {
qa_data['answer_vocab'][ans]: ans
for ans in qa_data['answer_vocab']
}
model_options = {
'num_lstm_layers': args.num_lstm_layers,
'rnn_size': args.rnn_size,
'embedding_size': args.embedding_size,
'word_emb_dropout': args.word_emb_dropout,
'image_dropout': args.image_dropout,
'fc7_feature_length': args.fc7_feature_length,
'lstm_steps': qa_data['max_question_length'] + 1,
'q_vocab_size': len(qa_data['question_vocab']),
'ans_vocab_size': len(qa_data['answer_vocab'])
}
model = vis_lstm_model.Vis_lstm_model(model_options)
input_tensors, t_prediction, t_ans_probab = model.build_generator()
sess = tf.InteractiveSession()
saver = tf.train.Saver()
avg_accuracy = 0.0
total = 0
saver.restore(sess, args.model_path)
batch_no = 0
while (batch_no * args.batch_size) < len(qa_data['validation']):
sentence, answer, fc7 = get_batch(batch_no, args.batch_size,
fc7_features, image_id_map, qa_data,
'val')
pred, ans_prob = sess.run([t_prediction, t_ans_probab],
feed_dict={
input_tensors['fc7']: fc7,
input_tensors['sentence']: sentence,
})
batch_no += 1
if args.debug:
for idx, p in enumerate(pred):
print ans_map[p], ans_map[np.argmax(answer[idx])]
correct_predictions = np.equal(pred, np.argmax(answer, 1))
correct_predictions = correct_predictions.astype('float32')
accuracy = correct_predictions.mean()
print "Acc", accuracy
avg_accuracy += accuracy
total += 1
print "Acc", avg_accuracy / total
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--num_lstm_layers',
type=int,
default=2,
help='num_lstm_layers')
parser.add_argument('--fc7_feature_length',
type=int,
default=4096,
help='fc7_feature_length')
parser.add_argument('--rnn_size', type=int, default=512, help='rnn_size')
parser.add_argument('--embedding_size',
type=int,
default=512,
help='embedding_size'),
parser.add_argument('--word_emb_dropout',
type=float,
default=0.5,
help='word_emb_dropout')
parser.add_argument('--image_dropout',
type=float,
default=0.5,
help='image_dropout')
parser.add_argument('--data_dir',
type=str,
default='Data',
help='Data directory')
parser.add_argument('--batch_size',
type=int,
default=200,
help='Batch Size')
parser.add_argument('--learning_rate',
type=float,
default=0.001,
help='Batch Size')
parser.add_argument('--epochs', type=int, default=200, help='Expochs')
parser.add_argument('--debug', type=bool, default=False, help='Debug')
parser.add_argument('--resume_model',
type=str,
default=None,
help='Trained Model Path')
parser.add_argument('--version',
type=int,
default=2,
help='VQA data version')
args = parser.parse_args()
print("Reading QA DATA")
qa_data = data_loader.load_questions_answers(args.version, args.data_dir)
print("Reading fc7 features")
fc7_features, image_id_list = data_loader.load_fc7_features(
args.data_dir, 'train')
print("FC7 features", fc7_features.shape)
print("image_id_list", image_id_list.shape)
image_id_map = {}
for i in range(len(image_id_list)):
image_id_map[image_id_list[i]] = i
ans_map = {
qa_data['answer_vocab'][ans]: ans
for ans in qa_data['answer_vocab']
}
model_options = {
'num_lstm_layers': args.num_lstm_layers,
'rnn_size': args.rnn_size,
'embedding_size': args.embedding_size,
'word_emb_dropout': args.word_emb_dropout,
'image_dropout': args.image_dropout,
'fc7_feature_length': args.fc7_feature_length,
'lstm_steps': qa_data['max_question_length'] + 1,
'q_vocab_size': len(qa_data['question_vocab']),
'ans_vocab_size': len(qa_data['answer_vocab'])
}
model = vis_lstm_model.Vis_lstm_model(model_options)
input_tensors, t_loss, t_accuracy, t_p = model.build_model()
train_op = tf.train.AdamOptimizer(args.learning_rate).minimize(t_loss)
sess = tf.InteractiveSession()
tf.initialize_all_variables().run()
saver = tf.train.Saver()
if args.resume_model:
saver.restore(sess, args.resume_model)
for i in range(args.epochs):
batch_no = 0
while (batch_no * args.batch_size) < len(qa_data['training']):
sentence, answer, fc7 = get_training_batch(batch_no,
args.batch_size,
fc7_features,
image_id_map, qa_data,
'train')
_, loss_value, accuracy, pred = sess.run(
[train_op, t_loss, t_accuracy, t_p],
feed_dict={
input_tensors['fc7']: fc7,
input_tensors['sentence']: sentence,
input_tensors['answer']: answer
})
batch_no += 1
if args.debug:
for idx, p in enumerate(pred):
print(ans_map[p], ans_map[np.argmax(answer[idx])])
print("Loss", loss_value, batch_no, i)
print("Accuracy", accuracy)
print("---------------")
else:
print("Loss", loss_value, batch_no, i)
print("Training Accuracy", accuracy)
save_path = saver.save(sess, "Data/Models/model{}.ckpt".format(i))
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--image_path',
type=str,
default='Data/train_2014/COCO_train2014_000000581922.jpg',
help='Image Path')
parser.add_argument(
'--model_path',
type=str,
default='Data/train2014/Tri Training 3/Models/model49.ckpt',
help='Model Path')
parser.add_argument('--num_lstm_layers',
type=int,
default=2,
help='num_lstm_layers')
parser.add_argument('--fc7_feature_length',
type=int,
default=4096,
help='fc7_feature_length')
parser.add_argument('--rnn_size', type=int, default=512, help='rnn_size')
parser.add_argument('--embedding_size',
type=int,
default=512,
help='embedding_size'),
parser.add_argument('--word_emb_dropout',
type=float,
default=0.5,
help='word_emb_dropout')
parser.add_argument('--image_dropout',
type=float,
default=0.5,
help='image_dropout')
parser.add_argument('--data_dir',
type=str,
default='Data/train2014/Tri Training 3/',
help='Data directory')
parser.add_argument('--batch_size',
type=int,
default=200,
help='Batch Size')
parser.add_argument('--question',
type=str,
default='What is this product?',
help='Question')
args = parser.parse_args()
#vizwiz_file_path = 'Data/Test'
solution = dict()
solution["model"] = "model_3"
solution["predictions"] = []
vocab_data = data_loader.get_question_answer_vocab(data_dir=args.data_dir)
qvocab = vocab_data['question_vocab']
q_map = {
vocab_data['question_vocab'][qw]: qw
for qw in vocab_data['question_vocab']
}
vizwiz_questions_path = 'VizWiz_to_VQA_Questions.json'
with open(vizwiz_questions_path, 'r') as input_file:
vizwiz_questions = json.loads(input_file.read())
'''with open('questions_temp.txt','w') as temp_file:
for i in range(20000):
#print(vizwiz_questions['questions'][i]['question']
temp_file.write(vizwiz_questions['questions'][i]['question'])
temp_file.write('\n')'''
question_vocab = vocab_data['question_vocab']
word_regex = re.compile(r'\w+')
#question_ids = np.zeros((20000, vocab_data['max_question_length']), dtype = 'int32')
#fc7_features = np.zeros((2, args.fc7_feature_length))
print("Reading fc7 features")
fc7_features, image_id_list = data_loader.load_fc7_features(
'Data/', 'train')
print("FC7 features", fc7_features.shape)
print("image_id_list", image_id_list.shape)
#print(0/0)
'''i=0
for file in os.listdir(vizwiz_file_path):
if file.endswith(".jpg"):
args.image_path = join(vizwiz_file_path,file)
#args.question = vizwiz_questions['questions'][i]['question']
print("Image:", args.image_path)
print("Question:", args.question)
fc7_features[i] = utils.extract_fc7_features(args.image_path, 'Data/vgg16-20160129.tfmodel')
i += 1'''
model_options = {
'num_lstm_layers': args.num_lstm_layers,
'rnn_size': args.rnn_size,
'embedding_size': args.embedding_size,
'word_emb_dropout': args.word_emb_dropout,
'image_dropout': args.image_dropout,
'fc7_feature_length': args.fc7_feature_length,
'lstm_steps': vocab_data['max_question_length'] + 1,
'q_vocab_size': len(vocab_data['question_vocab']),
'ans_vocab_size': len(vocab_data['answer_vocab'])
}
#question_words = re.findall(word_regex, args.question)
#base = vocab_data['max_question_length'] - len(question_words)
'''for no_questions in range(question_ids.shape[0]):
for i in range(0, len(question_words)):
if question_words[i] in question_vocab:
question_ids[no_questions][base + i] = question_vocab[ question_words[i] ]
else:
question_ids[no_questions][base + i] = question_vocab['UNK']'''
ans_map = {
vocab_data['answer_vocab'][ans]: ans
for ans in vocab_data['answer_vocab']
}
model = vis_lstm_model.Vis_lstm_model(model_options)
input_tensors, t_prediction, t_ans_probab = model.build_generator()
sess = tf.InteractiveSession()
saver = tf.train.Saver()
saver.restore(sess, args.model_path)
batch_no = 0
with open('result3.txt', 'w') as output_file:
while (batch_no * args.batch_size) < 20000:
question_ids = np.zeros(
(args.batch_size, vocab_data['max_question_length']),
dtype='int32')
#vizwiz_questions['questions'][i]['question']
for no_questions in range(question_ids.shape[0]):
question_formatted = vizwiz_questions['questions'][
batch_no * args.batch_size + no_questions]['question']
question_list = question_formatted.split()
question_list = question_list[0:20]
question_formatted = ' '.join(question_list)
question_words = re.findall(word_regex, question_formatted)
base = vocab_data['max_question_length'] - len(question_words)
for i in range(0, len(question_words)):
if question_words[i] in question_vocab:
question_ids[no_questions][base + i] = question_vocab[
question_words[i]]
else:
question_ids[no_questions][base +
i] = question_vocab['UNK']
fc7 = get_batch(batch_no, args.batch_size, fc7_features)
pred, ans_prob = sess.run(
[t_prediction, t_ans_probab],
feed_dict={
input_tensors['fc7']: fc7,
input_tensors['sentence']: question_ids,
})
for i in range(len(pred)):
current_prediction = dict()
current_prediction["image_id"] = "VizWiz_train_%.12d.jpg" % (
batch_no * args.batch_size + i)
current_prediction["question"] = vizwiz_questions['questions'][
batch_no * args.batch_size + i]['question']
#output_file.write("Ques:" + vizwiz_questions['questions'][batch_no*args.batch_size + i]['question'])
answer_list = []
answer_probab_tuples = [(-ans_prob[i][idx], idx)
for idx in range(len(ans_prob[0]))]
answer_probab_tuples.sort()
for j in range(5):
answer_list.append(ans_map[answer_probab_tuples[j][1]])
#output_file.write("Ans:" + ans_map[pred[i]])
current_prediction["predicted_answer"] = answer_list
#output_file.write("Ans:" + str(answer_list))
#output_file.write('\n')
solution["predictions"].append(current_prediction)
#print("Ans:", ans_map[pred[i]])
#print('\n')
batch_no += 1
output_file.write(json.dumps(solution))
