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extract_fc7.py
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extract_fc7.py
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import tensorflow as tf
from scipy import misc
from os import listdir
from os.path import isfile, join
import data_loader
import utils
import argparse
import numpy as np
import pickle
import h5py
import time
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--split', type=str, default='train',
help='train/val')
parser.add_argument('--model_path', type=str, default='Data/vgg16.tfmodel',
help='Pretrained VGG16 Model')
parser.add_argument('--data_dir', type=str, default='Data',
help='Data directory')
parser.add_argument('--batch_size', type=int, default=10,
help='Batch Size')
args = parser.parse_args()
vgg_file = open(args.model_path)
vgg16raw = vgg_file.read()
vgg_file.close()
graph_def = tf.GraphDef()
graph_def.ParseFromString(vgg16raw)
images = tf.placeholder("float", [None, 224, 224, 3])
tf.import_graph_def(graph_def, input_map={ "images": images })
graph = tf.get_default_graph()
for opn in graph.get_operations():
print "Name", opn.name, opn.values()
all_data = data_loader.load_questions_answers(args)
if args.split == "train":
qa_data = all_data['training']
else:
qa_data = all_data['validation']
image_ids = {}
for qa in qa_data:
image_ids[qa['image_id']] = 1
image_id_list = [img_id for img_id in image_ids]
print "Total Images", len(image_id_list)
sess = tf.Session()
fc7 = np.ndarray( (len(image_id_list), 4096 ) )
idx = 0
while idx < len(image_id_list):
start = time.clock()
image_batch = np.ndarray( (args.batch_size, 224, 224, 3 ) )
count = 0
for i in range(0, args.batch_size):
if idx >= len(image_id_list):
break
image_file = join(args.data_dir, '%s2014/COCO_%s2014_%.12d.jpg'%(args.split, args.split, image_id_list[idx]) )
image_batch[i,:,:,:] = utils.load_image_array(image_file)
idx += 1
count += 1
feed_dict = { images : image_batch[0:count,:,:,:] }
fc7_tensor = graph.get_tensor_by_name("import/Relu_1:0")
fc7_batch = sess.run(fc7_tensor, feed_dict = feed_dict)
fc7[(idx - count):idx, :] = fc7_batch[0:count,:]
end = time.clock()
print "Time for batch 10 photos", end - start
print "Hours For Whole Dataset" , (len(image_id_list) * 1.0)*(end - start)/60.0/60.0/10.0
print "Images Processed", idx
print "Saving fc7 features"
h5f_fc7 = h5py.File( join(args.data_dir, args.split + '_fc7.h5'), 'w')
h5f_fc7.create_dataset('fc7_features', data=fc7)
h5f_fc7.close()
print "Saving image id list"
h5f_image_id_list = h5py.File( join(args.data_dir, args.split + '_image_id_list.h5'), 'w')
h5f_image_id_list.create_dataset('image_id_list', data=image_id_list)
h5f_image_id_list.close()
print "Done!"
if __name__ == '__main__':
main()