retraining last layer of inception-v3 significantly slowers the classification - python-3.x

Question

In an attempt for transfer learning over inception-v3 with TF and PY3.5, I've tested two approaches:
1- retraining the last layer, as shown here: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/image_retraining
2- Apply linear SVM on top of inception-V3 bottlenecks as demonstrated here: https://www.kernix.com/blog/image-classification-with-a-pre-trained-deep-neural-network_p11
Expectedly, they should've had a similar runtime for classification phase, since the critical part - the bottlenecks extraction - is identical. In practice though, the retrained network is about 8X slower when running classification.
My questions is whether anyone has an idea for the reason of this.
Some code snippets:
SVM on top (the faster):
def getTensors():
graph_def = tf.GraphDef()
f = open('classify_image_graph_def.pb', 'rb')
graph_def.ParseFromString(f.read())
tensorBottleneck, tensorsResizedImage = tf.import_graph_def(graph_def, name='', return_elements=['pool_3/_reshape:0', 'Mul:0'])
return tensorBottleneck, tensorsResizedImage
def calc_bottlenecks(imgFile, tensorBottleneck, tensorsResizedImage):
""" - read, decode and resize to get <resizedImage> - """
bottleneckValues = sess.run(tensorBottleneck, {tensorsResizedImage : resizedImage})
return np.squeeze(bottleneckValues)
This takes about 0.5 sec on my (Windows) laptop while the SVM part takes no time.
Retraining last layer - (this is harder to summarize since longer code)
def loadGraph(pbFile):
with tf.gfile.FastGFile(pbFile, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
with tf.Session() as sess:
softmaxTensor = sess.graph.get_tensor_by_name('final_result:0')
def labelImage(imageFile, softmaxTensor):
with tf.Session() as sess:
input_layer_name = 'DecodeJpeg/contents:0'
predictions, = sess.run(softmax_tensor, {input_layer_name: image_data})
'pbFile' is the file saved be the retrainer, which supposed to have identical topology and weights excluding the classification layer, as 'classify_image_graph_def.pb'. This takes about 4sec to run (on my same laptop, without the loading).
Any idea for the performance gap?
Thanks!

Answer 1

Solved. The problem was in creating a new tf.Session() for every image. Storing the session when reading graph and using it made runtime back to expected.
def loadGraph(pbFile):
...
with tf.Session() as sess:
softmaxTensor = sess.graph.get_tensor_by_name('final_result:0')
sessToStore = sess
return softmaxTensor, sessToStore
def labelImage(imageFile, softmaxTensor, sessToStore):
input_layer_name = 'DecodeJpeg/contents:0'
predictions, = sessToStore.run(softmax_tensor, {input_layer_name: image_data})