classify.py

import sys
import time
import numpy as np
import tensorflow as tf


def load_graph(model_file):
    graph = tf.Graph()
    graph_def = tf.GraphDef()

    with open(model_file, "rb") as f:
        graph_def.ParseFromString(f.read())
    with graph.as_default():
        tf.import_graph_def(graph_def)
    return graph


def read_tensor_from_image_file(file_name, input_height=227, input_width=227, input_mean=0, input_std=255):
    input_name = "file_reader"
    output_name = "normalized"
    file_reader = tf.read_file(file_name, input_name)
    if file_name.endswith(".png"):
        image_reader = tf.image.decode_png(file_reader, channels=3, name='png_reader')
    elif file_name.endswith(".gif"):
        image_reader = tf.squeeze(tf.image.decode_gif(file_reader, name='gif_reader'))
    elif file_name.endswith(".bmp"):
        image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
    else:
        image_reader = tf.image.decode_jpeg(file_reader, channels=3, name='jpeg_reader')
    float_caster = tf.cast(image_reader, tf.float32)
    dims_expander = tf.expand_dims(float_caster, 0)
    resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
    normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
    sess = tf.Session()
    result = sess.run(normalized)
    return result


def load_labels(label_file):
    label = []
    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
    for l in proto_as_ascii_lines:
        label.append(l.rstrip())
    return label


if __name__ == "__main__":
    file_name = sys.argv[1]
    model_file = "res/retrained_graph.pb"
    label_file = "res/retrained_labels.txt"
    input_height = 224
    input_width = 224
    input_mean = 128
    input_std = 128
    input_layer = "input"
    output_layer = "final_result"
    graph = load_graph(model_file)
    t = read_tensor_from_image_file(file_name,
                                    input_height=input_height,
                                    input_width=input_width,
                                    input_mean=input_mean,
                                    input_std=input_std)

    input_name = "import/" + input_layer
    output_name = "import/" + output_layer
    input_operation = graph.get_operation_by_name(input_name)
    output_operation = graph.get_operation_by_name(output_name)

    with tf.Session(graph=graph) as sess:
        start = time.time()
        results = sess.run(output_operation.outputs[0],
                           {input_operation.outputs[0]: t})
        end = time.time()

    results = np.squeeze(results)
    top_k = results.argsort()[-5:][::-1]
    labels = load_labels(label_file)

    print('\nEvaluation time (1-image): {:.3f}s\n'.format(end-start))
    template = "{} (score={:0.5f})"
    for i in top_k:
        print(template.format(labels[i], results[i]))