def get_conv_features(image_file, model_type, feature_layer):
if model_type == "vgg":
cnn_model = vgg16.create_vgg_model(448,
only_conv=feature_layer != 'fc7')
else:
cnn_model = resnet.create_resnet_model(448)
sess = cnn_model['session']
images = cnn_model['images_placeholder']
image_feature_layer = cnn_model[feature_layer]
img_dim = 448
if model_type == 'resnet':
image_array = sess.run(cnn_model['processed_image'],
feed_dict={
cnn_model['pre_image']:
utils.load_image_array(image_file,
img_dim=None)
})
else:
image_array = utils.load_image_array(image_file, img_dim=img_dim)
feed_dict = {images: [image_array]}
conv_features_batch = sess.run(image_feature_layer, feed_dict=feed_dict)
sess.close()
return conv_features_batch
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--split',
type=str,
default='train',
help='train/val/test')
parser.add_argument('--batch_size',
type=int,
default=64,
help='Batch Size')
parser.add_argument('--feature_layer',
type=str,
default="block4",
help='CONV FEATURE LAYER, fc7, pool5 or block4')
parser.add_argument('--model', type=str, default="resnet", help='vgg')
args = parser.parse_args()
if args.split == "train":
with open('Data/annotations/test.json') as f:
images = json.loads(f.read())['images']
else:
with open('Data/annotations/captions_val2014.json') as f:
images = json.loads(f.read())['images']
image_ids = {image['image_id']: 1 for image in images}
image_id_list = [img_id for img_id in image_ids]
print("Total Images", len(image_id_list))
try:
shutil.rmtree('Data/conv_features_{}_{}'.format(
args.split, args.model))
except:
pass
os.makedirs('Data/conv_features_{}_{}'.format(args.split, args.model))
if args.model == "vgg":
cnn_model = vgg16.create_vgg_model(
448, only_conv=args.feature_layer != 'fc7')
else:
cnn_model = resnet.create_resnet_model(448)
image_id_file_name = "Data/conv_features_{}_{}/image_id_list_{}.h5".format(
args.split, args.model, args.feature_layer)
h5f_image_id_list = h5py.File(image_id_file_name, 'w')
h5f_image_id_list.create_dataset('image_id_list', data=image_id_list)
h5f_image_id_list.close()
conv_file_name = "Data/conv_features_{}_{}/conv_features_{}.h5".format(
args.split, args.model, args.feature_layer)
hdf5_conv_file = h5py.File(conv_file_name, 'w')
if args.feature_layer == "fc7":
conv_features = None
feature_shape = (len(image_id_list), 4096)
img_dim = 224
else:
if args.model == "vgg":
conv_features = None
feature_shape = (len(image_id_list), 14, 14, 512)
img_dim = 448
else:
conv_features = None
feature_shape = (len(image_id_list), 14, 14, 2048)
img_dim = 448
print("it's done!!!")
hdf5_data = hdf5_conv_file.create_dataset('conv_features',
shape=feature_shape,
dtype='f')
sess = cnn_model['session']
images = cnn_model['images_placeholder']
image_feature_layer = cnn_model[args.feature_layer]
idx = 0
while idx < len(image_id_list):
start = time.clock()
image_batch = np.ndarray((args.batch_size, img_dim, img_dim, 3))
count = 0
for i in range(0, args.batch_size):
if idx >= len(image_id_list):
break
image_file = ('Data/images/abstract_v002_%s2015_%.12d.jpg' %
(args.split, image_id_list[idx]))
if args.model == 'resnet':
image_array = sess.run(cnn_model['processed_image'],
feed_dict={
cnn_model['pre_image']:
utils.load_image_array(image_file,
img_dim=None)
})
else:
image_array = utils.load_image_array(image_file,
img_dim=img_dim)
image_batch[i, :, :, :] = image_array
idx += 1
count += 1
feed_dict = {images: image_batch[0:count, :, :, :]}
conv_features_batch = sess.run(image_feature_layer,
feed_dict=feed_dict)
#np.reshape not needed
#conv_features_batch = np.reshape(conv_features_batch, ( conv_features_batch.shape[0], -1 ))
hdf5_data[(idx - count):idx] = conv_features_batch[0:count]
end = time.clock()
print("Time for batch of photos", end - start)
print("Hours Remaining", ((len(image_id_list) - idx) * 1.0) *
(end - start) / 60.0 / 60.0 / args.batch_size)
print("Images Processed", idx)
hdf5_conv_file.close()
print("Done!")