text: #
  # -*- coding: utf-8 -*-
  #
  # Copyright (c) 2019 Intel Corporation
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  #
  
  import os
  import io
  import skimage.io
  import glob
  import numpy as np
  import tensorflow.compat.v1 as tf
  from PIL import Image
  import time
  from lpot.experimental import Quantization
  from lpot.data import DATALOADERS, DATASETS
  from lpot.adaptor.tf_utils.util import _parse_ckpt_bn_input
  
  flags = tf.flags
  flags.DEFINE_string('style_images_paths', None, 'Paths to the style images'
                      'for evaluation.')
  flags.DEFINE_string('content_images_paths', None, 'Paths to the content images'
                      'for evaluation.')
  flags.DEFINE_string('output_dir', './result', 'Output stylized image directory.')
  
  flags.DEFINE_string('output_model', None, 'Output model directory.')
  
  flags.DEFINE_string('input_model', None, 'Output directory.')
  
  flags.DEFINE_integer('batch_size', 1, 'batch_size')
  
  flags.DEFINE_bool('tune', False, 'if use tune')
  
  flags.DEFINE_string('config', None, 'yaml configuration for tuning')
  
  FLAGS = flags.FLAGS
  
  def load_img(path, resize_shape=(256, 256), crop_ratio=0.1):
      img = Image.open(path)
      width, height = img.size
      crop_box = (crop_ratio*height, crop_ratio*width, (1-crop_ratio)*height, (1-crop_ratio)*width)
      img = np.asarray(img.crop(crop_box).resize(resize_shape))
      if img.max() > 1.0:
          img = img / 255.
      img = img.astype(np.float32)[np.newaxis, ...]
      return img
  
  def save_image(image, output_file, save_format='jpeg'):
      image = np.uint8(image * 255.0)
      buf = io.BytesIO()
      skimage.io.imsave(buf, np.squeeze(image, 0), format=save_format)
      buf.seek(0)
      f = tf.gfile.GFile(output_file, 'w')
      f.write(buf.getvalue())
      f.close()
  
  def image_style_transfer(sess, content_img_path, style_img_path):
      stylized_images = sess.graph.get_tensor_by_name('import/import/transformer/expand/conv3/conv/Sigmoid:0')
      style_img_np = load_img(style_img_path, crop_ratio=0)
      content_img_np = load_img(content_img_path, crop_ratio=0)
      stylized_image_res = sess.run(
          stylized_images,
          feed_dict={
              'import/import/style_input:0': style_img_np,
              'import/import/content_input:0': content_img_np})
      # saves stylized image.
      save_image(stylized_image_res, os.path.join(FLAGS.output_dir, 'stylized_image.jpg'))
  
  def main(args=None):
    tf.logging.set_verbosity(tf.logging.INFO)
    if not tf.gfile.Exists(FLAGS.output_dir):
      tf.gfile.MkDir(FLAGS.output_dir)
  
    with tf.Session() as sess:
        if FLAGS.input_model.rsplit('.', 1)[-1] == 'ckpt':
            style_img_ph = tf.placeholder(tf.float32, shape=[None, 256, 256, 3], name='style_input')
            content_img_ph = tf.placeholder(tf.float32, shape=[None, 256, 256, 3], name='content_input')
            # import meta_graph
            meta_data_path = FLAGS.input_model + '.meta'
            saver = tf.train.import_meta_graph(meta_data_path, clear_devices=True)
  
            sess.run(tf.global_variables_initializer())
            saver.restore(sess, FLAGS.input_model)
            graph_def = sess.graph.as_graph_def()
  
            replace_style = 'style_image_processing/ResizeBilinear_2'
            replace_content = 'batch_processing/batch'
            for node in graph_def.node:
                for idx, input_name in enumerate(node.input):
                    # replace style input and content input nodes to  placeholder
                    if replace_content == input_name:
                        node.input[idx] = 'content_input'
                    if replace_style == input_name:
                        node.input[idx] = 'style_input'
  
            if FLAGS.tune:
                _parse_ckpt_bn_input(graph_def)
            output_name = 'transformer/expand/conv3/conv/Sigmoid'
            frozen_graph = tf.graph_util.convert_variables_to_constants(sess, graph_def, [output_name])
        # use frozen pb instead 
        elif FLAGS.input_model.rsplit('.', 1)[-1] == 'pb':
            with open(FLAGS.input_model, 'rb') as f:
                frozen_graph = tf.GraphDef()
                frozen_graph.ParseFromString(f.read())
        else:
            print("not supported model format")
            exit(-1)
  
        if FLAGS.tune:
            with tf.Graph().as_default() as graph:
                tf.import_graph_def(frozen_graph, name='')
                quantizer = Quantization(FLAGS.config)
                quantizer.model = graph
                quantized_model = quantizer()
                quantized_model.save(FLAGS.output_model)
                frozen_graph= quantized_model.graph_def
  
    # validate the quantized model here
    with tf.Graph().as_default(), tf.Session() as sess:
        if FLAGS.tune:
            # create dataloader using default style_transfer dataset
            # generate stylized images
            dataset = DATASETS('tensorflow')['style_transfer']( \
                FLAGS.content_images_paths.strip(),
                FLAGS.style_images_paths.strip(),
                crop_ratio=0.2,
                resize_shape=(256, 256))
        else: 
            dataset = DATASETS('tensorflow')['dummy_v2'](\
                input_shape=[(256, 256, 3), (256, 256, 3)], label_shape=(1, )) 
        dataloader = DATALOADERS['tensorflow'](dataset=dataset, batch_size=FLAGS.batch_size)
        tf.import_graph_def(frozen_graph, name='')
        style_transfer(sess, dataloader)
  
  def add_import_to_name(sess, name, try_cnt=2):
      for i in range(0, try_cnt):
          try:
              sess.graph.get_tensor_by_name(name)   
              return name
          except:
              name = 'import/' + name
  
      raise ValueError('can not find tensor by name')
  
  # validate and  save the files
  def style_transfer(sess, dataloader):
        time_list = []
        output_name = add_import_to_name(sess, 'transformer/expand/conv3/conv/Sigmoid:0', 3)
        style_name = add_import_to_name(sess, 'style_input:0', 3)
        content_name = add_import_to_name(sess, 'content_input:0', 3)
  
        stylized_images = sess.graph.get_tensor_by_name(output_name)
        
        for idx, ((content_img_np, style_img_np), _) in enumerate(dataloader):
            start_time = time.time()
            stylized_image_res = sess.run(
                stylized_images,
                feed_dict={
                    style_name: style_img_np,
                    content_name: content_img_np})
            duration = time.time() - start_time
            time_list.append(duration)
            if idx + 1 == 20:
                break
        warm_up = 1
        throughput = (len(time_list) - warm_up)/ np.array(time_list[warm_up:]).sum()
        print('Batch size = {}'.format(FLAGS.batch_size)) 
        print('Latency: {:.3f} ms'.format(np.array(time_list[warm_up:]).mean() * 1000)) 
        print('Throughput: {:.3f} images/sec'.format(throughput)) 
  
  
  def run_tuning():
    tf.disable_v2_behavior()
    tf.app.run(main)
  
  if __name__ == '__main__':
    run_tuning()
  