问__init__()在tensorflow中缺少1个必需的位置参数：“sess”

EN

def image_recognition_algorithm():

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=299, input_width=299,
                input_mean=0, input_std=255):
    input_name = "file_reader"
    output_name = "normalized"
    file_reader = tf.read_file(file_name, input_name)
    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

class initiate_session():

def __init__(self, sess):
    self.sess = sess
    graph = load_graph(model_file)
    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);

    config = tf.ConfigProto(device_count={"CPU": 4},
                            inter_op_parallelism_threads=1,
                            intra_op_parallelism_threads=4)
    self.sess = tf.Session(graph=graph, config = config)
    start = time.time()
    results = self.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))


    for i in top_k:
        print(file_name, labels[i], results[i])

    return [file_name] + list(results)

    image_list = [f for f in listdir('test_images') if isfile(join('test_images', f))]

    res_list = []
    for image in image_list:
        if image.lower().endswith(('.png', '.jpg', '.jpeg', '.gif')):
            res_list.append(main(join('test_images', image)))

def main(self, file_name):
    model_file = "tf_files/retrained_graph.pb"
    label_file = "tf_files/retrained_labels.txt"
    input_height = 299
    input_width = 299
    input_mean = 128
    input_std = 128
    input_layer = "Mul"
    output_layer = "final_result"

    t = read_tensor_from_image_file(file_name,
                                    input_height=input_height,
                                    input_width=input_width,
                                    input_mean=input_mean,
                                    input_std=input_std)

if __name__ == '__main__':
    initiate_session().main()