#
# -*- coding: utf-8 -*-
#
# Copyright (c) 2018 Intel Corporation
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# 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
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#    http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# limitations under the License.
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#
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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
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# http://www.apache.org/licenses/LICENSE-2.0
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# 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.
# ==============================================================================
"""Generic evaluation script that evaluates a model using a given dataset."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math
import tensorflow as tf
import os
import time
from datetime import datetime

from datasets import dataset_factory
from nets import nets_factory
from preprocessing import preprocessing_factory

slim = tf.contrib.slim

tf.app.flags.DEFINE_integer(
    'batch_size', 100, 'The number of samples in each batch.')

tf.app.flags.DEFINE_integer(
    'max_num_batches', 1,
    'Max number of batches to evaluate by default use all.')

tf.app.flags.DEFINE_string(
    'master', '', 'The address of the TensorFlow master to use.')

tf.app.flags.DEFINE_string(
    'checkpoint_path', '/tmp/tfmodel/',
    'The directory where the model was written to or an absolute path to a '
    'checkpoint file.')

tf.app.flags.DEFINE_string(
    'eval_dir', '/tmp/tfmodel/', 'Directory where the results are saved to.')

tf.app.flags.DEFINE_integer(
    'num_preprocessing_threads', 4,
    'The number of threads used to create the batches.')

tf.app.flags.DEFINE_string(
    'dataset_name', 'imagenet', 'The name of the dataset to load.')

tf.app.flags.DEFINE_string(
    'dataset_split_name', 'test', 'The name of the train/test split.')

tf.app.flags.DEFINE_string(
    'dataset_dir', None, 'The directory where the dataset files are stored.')

tf.app.flags.DEFINE_integer(
    'labels_offset', 0,
    'An offset for the labels in the dataset. This flag is primarily used to '
    'evaluate the VGG and ResNet architectures which do not use a background '
    'class for the ImageNet dataset.')

tf.app.flags.DEFINE_string(
    'model_name', 'inception_v3', 'The name of the architecture to evaluate.')

tf.app.flags.DEFINE_string(
    'preprocessing_name', None, 'The name of the preprocessing to use. If left '
    'as `None`, then the model_name flag is used.')

tf.app.flags.DEFINE_float(
    'moving_average_decay', None,
    'The decay to use for the moving average.'
    'If left as None, then moving averages are not used.')

tf.app.flags.DEFINE_integer(
    'eval_image_size', None, 'Eval image size')

tf.app.flags.DEFINE_integer(
    'eval_log_frequency', 10, 'Number of eval steps to run between displaying '
     'eval metrics.')

tf.app.flags.DEFINE_integer(
    'inter_op_parallelism_threads', 1, 'The number of inter-thread.')

tf.app.flags.DEFINE_integer(
    'intra_op_parallelism_threads', 28, 'The number of intra-thread.')


FLAGS = tf.app.flags.FLAGS

class _LoggerHook(tf.train.SessionRunHook):
  """ Logs loss and runtime."""

  def begin(self):
    self._step = -1
    self._displayed_steps = 0
    self._total_images_per_sec = 0

  def before_run(self, run_context):
    self._step += 1
    self._start_time = time.time()

  def after_run(self, run_context, run_values):
    duration = time.time() - self._start_time
    if (self._step + 1) % FLAGS.eval_log_frequency == 0:
      images_per_sec = FLAGS.batch_size / duration
      self._displayed_steps += 1
      self._total_images_per_sec += images_per_sec

      format_str = ('%s: step %d, %.1f images/sec')
      print (format_str % (datetime.now(), (self._step+1), images_per_sec))

  def end(self, run_context):
    print('self._total_images_per_sec = %.1f' % self._total_images_per_sec)
    print('self._displayed_steps = %d' % self._displayed_steps)
    images_per_sec = self._total_images_per_sec / self._displayed_steps
    print('Total images/sec = %.1f' %(images_per_sec))
    if FLAGS.batch_size == 1:
      latency = 1000 / images_per_sec
      print('Latency ms/step = %.1f' % (latency))

def main(_):
  tf.logging.set_verbosity(tf.logging.INFO)

  with tf.Graph().as_default():
    tf_global_step = slim.get_or_create_global_step()

    ######################
    # Select the dataset #
    ######################
    if FLAGS.dataset_dir:
        print("Inference using real data")
        dataset = dataset_factory.get_dataset(
            FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)
        num_classes = dataset.num_classes - FLAGS.labels_offset
    else:
        print("Inference using synthetic data")
        num_classes = 1000

    ####################
    # Select the model #
    ####################
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=num_classes,
        is_training=False)

    eval_image_size = FLAGS.eval_image_size or network_fn.default_image_size

    if FLAGS.dataset_dir:
        ##############################################################
        # Create a dataset provider that loads data from the dataset #
        ##############################################################
        provider = slim.dataset_data_provider.DatasetDataProvider(
            dataset,
            shuffle=False,
            common_queue_capacity=2 * FLAGS.batch_size,
            common_queue_min=FLAGS.batch_size)
        [image, label] = provider.get(['image', 'label'])
        label -= FLAGS.labels_offset

        #####################################
        # Select the preprocessing function #
        #####################################
        preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
        image_preprocessing_fn = preprocessing_factory.get_preprocessing(
            preprocessing_name,
            is_training=False)

        image = image_preprocessing_fn(image, eval_image_size, eval_image_size)

        images, labels = tf.train.batch(
            [image, label],
            batch_size=FLAGS.batch_size,
            num_threads=FLAGS.num_preprocessing_threads,
            capacity=5 * FLAGS.batch_size)
    else:
        # Generate random images and labels with constant 0 when no dataset is used
        input_shape = [FLAGS.batch_size, eval_image_size, eval_image_size, 3]
        label_shape = [FLAGS.batch_size]
        images = tf.random.uniform(input_shape, 0.0, 255.0, dtype=tf.float32, name='synthetic_images')
        labels = tf.constant(0, shape=label_shape, dtype=tf.int64)

    ####################
    # Define the model #
    ####################
    logits, _ = network_fn(images)

    if FLAGS.moving_average_decay:
      variable_averages = tf.train.ExponentialMovingAverage(
          FLAGS.moving_average_decay, tf_global_step)
      variables_to_restore = variable_averages.variables_to_restore(
          slim.get_model_variables())
      variables_to_restore[tf_global_step.op.name] = tf_global_step
    else:
      variables_to_restore = slim.get_variables_to_restore()

    predictions = tf.argmax(logits, 1)
    #labels = tf.squeeze(labels)

    # Define the metrics:
    names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
        'Accuracy': slim.metrics.streaming_accuracy(predictions, labels),
        'Recall_5': slim.metrics.streaming_recall_at_k(
            logits, labels, 5),
    })

    # Print the summaries to screen.
    for name, value in names_to_values.items():
      summary_name = 'eval/%s' % name
      op = tf.summary.scalar(summary_name, value, collections=[])
      op = tf.Print(op, [value], summary_name)
      tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)

    # TODO(sguada) use num_epochs=1
    if FLAGS.max_num_batches:
      num_batches = FLAGS.max_num_batches
    else:
      # This ensures that we make a single pass over all of the data.
      num_batches = math.ceil(dataset.num_samples / float(FLAGS.batch_size))

    num_batches = 100

    config = tf.ConfigProto(inter_op_parallelism_threads=FLAGS.inter_op_parallelism_threads, intra_op_parallelism_threads=FLAGS.intra_op_parallelism_threads)

    if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
      checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
    else:
      checkpoint_path = FLAGS.checkpoint_path

    tf.logging.info('Evaluating %s' % checkpoint_path)

    slim.evaluation.evaluate_once(
        master=FLAGS.master,
        checkpoint_path=checkpoint_path,
        logdir=FLAGS.eval_dir,
        num_evals=num_batches,
        eval_op=list(names_to_updates.values()),
        variables_to_restore=variables_to_restore,
        hooks=[_LoggerHook()],
        session_config=config)


if __name__ == '__main__':
  tf.app.run()
