inference.py

import numpy as np
import tensorflow as tf

# imagePath = '/media/jai/Local drive A/tmp/imagenet/image1.jpg'
# modelFullPath = '/media/jai/Local drive A/tmp/imagenet/output_graph.pb'
# labelsFullPath = '/media/jai/Local drive A/tmp/imagenet/output_labels.txt'

imagePath = './a.png'
modelFullPath = './classify.pb'
labelsFullPath = './labels.txt'


def create_graph():
    """Creates a graph from saved GraphDef file and returns a saver."""
    # Creates graph from saved graph_def.pb.
    with tf.gfile.FastGFile(modelFullPath, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='')
def run_inference_on_image():
    answer = None

    if not tf.gfile.Exists(imagePath):
        tf.logging.fatal('File does not exist %s', imagePath)
        return answer

    image_data = tf.gfile.FastGFile(imagePath, 'rb').read()

    # Creates graph from saved GraphDef.
    create_graph()

    with tf.Session() as sess:

        softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
        predictions = sess.run(softmax_tensor,
                               {'DecodeJpeg/contents:0': image_data})
        predictions = np.squeeze(predictions)

        top_k = predictions.argsort()[-1:][::-1]  # Getting top 5 predictions
        f = open(labelsFullPath, 'rb')
        lines = f.readlines()
        labels = [str(w).replace("\n", "") for w in lines]
        for node_id in top_k:
            human_string = labels[node_id]
            score = predictions[node_id]

        answer = labels[top_k[0]]
        return score

def run_inference_on_image2():
    answer = None

    if not tf.gfile.Exists(imagePath):
        tf.logging.fatal('File does not exist %s', imagePath)
        return answer

    image_data = tf.gfile.FastGFile(imagePath, 'rb').read()

    # Creates graph from saved GraphDef.

    with tf.Session() as sess:

        softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
        predictions = sess.run(softmax_tensor,
                               {'DecodeJpeg/contents:0': image_data})
        predictions = np.squeeze(predictions)

        top_k = predictions.argsort()[-1:][::-1]  # Getting top 5 predictions
        f = open(labelsFullPath, 'rb')
        lines = f.readlines()
        labels = [str(w).replace("\n", "") for w in lines]
        for node_id in top_k:
            human_string = labels[node_id]
            score = predictions[node_id]

        answer = labels[top_k[0]]
        return answer


if __name__ == '__main__':
    run_inference_on_image()