imagenet_main.py

# Copyright 2017 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
#
#     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.
# ==============================================================================
"""Runs a ResNet model on the ImageNet dataset."""

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

import os
import sys

import tensorflow as tf

import resnet_model
import resnet_shared
import vgg_preprocessing

_DEFAULT_IMAGE_SIZE = 224
_NUM_CHANNELS = 3
_NUM_CLASSES = 1001

_NUM_IMAGES = {
    'train': 1281167,
    'validation': 50000,
}

_FILE_SHUFFLE_BUFFER = 1024
_SHUFFLE_BUFFER = 1500


###############################################################################
# Data processing
###############################################################################
def filenames(is_training, data_dir):
  """Return filenames for dataset."""
  if is_training:
    return [
        os.path.join(data_dir, 'train-%05d-of-01024' % i)
        for i in range(1024)]
  else:
    return [
        os.path.join(data_dir, 'validation-%05d-of-00128' % i)
        for i in range(128)]


def parse_record(raw_record, is_training):
  """Parse an ImageNet record from `value`."""
  keys_to_features = {
      'image/encoded':
          tf.FixedLenFeature((), tf.string, default_value=''),
      'image/format':
          tf.FixedLenFeature((), tf.string, default_value='jpeg'),
      'image/class/label':
          tf.FixedLenFeature([], dtype=tf.int64, default_value=-1),
      'image/class/text':
          tf.FixedLenFeature([], dtype=tf.string, default_value=''),
      'image/object/bbox/xmin':
          tf.VarLenFeature(dtype=tf.float32),
      'image/object/bbox/ymin':
          tf.VarLenFeature(dtype=tf.float32),
      'image/object/bbox/xmax':
          tf.VarLenFeature(dtype=tf.float32),
      'image/object/bbox/ymax':
          tf.VarLenFeature(dtype=tf.float32),
      'image/object/class/label':
          tf.VarLenFeature(dtype=tf.int64),
  }

  parsed = tf.parse_single_example(raw_record, keys_to_features)

  image = tf.image.decode_image(
      tf.reshape(parsed['image/encoded'], shape=[]),
      _NUM_CHANNELS)
  image = tf.image.convert_image_dtype(image, dtype=tf.float32)

  image = vgg_preprocessing.preprocess_image(
      image=image,
      output_height=_DEFAULT_IMAGE_SIZE,
      output_width=_DEFAULT_IMAGE_SIZE,
      is_training=is_training)

  label = tf.cast(
      tf.reshape(parsed['image/class/label'], shape=[]),
      dtype=tf.int32)

  return image, tf.one_hot(label, _NUM_CLASSES)


def input_fn(is_training, data_dir, batch_size, num_epochs=1):
  """Input function which provides batches for train or eval."""
  dataset = tf.data.Dataset.from_tensor_slices(
      filenames(is_training, data_dir))

  if is_training:
    dataset = dataset.shuffle(buffer_size=_FILE_SHUFFLE_BUFFER)

  dataset = dataset.flat_map(tf.data.TFRecordDataset)
  dataset = dataset.map(lambda value: parse_record(value, is_training),
                        num_parallel_calls=5)
  dataset = dataset.prefetch(batch_size)

  if is_training:
    # When choosing shuffle buffer sizes, larger sizes result in better
    # randomness, while smaller sizes have better performance.
    dataset = dataset.shuffle(buffer_size=_SHUFFLE_BUFFER)

  # We call repeat after shuffling, rather than before, to prevent separate
  # epochs from blending together.
  dataset = dataset.repeat(num_epochs)
  dataset = dataset.batch(batch_size)

  iterator = dataset.make_one_shot_iterator()
  images, labels = iterator.get_next()
  return images, labels


###############################################################################
# Running the model
###############################################################################
class ImagenetModel(resnet_model.Model):
  def __init__(self, resnet_size, data_format=None):
    """These are the parameters that work for Imagenet data.
    """

    # For bigger models, we want to use "bottleneck" layers
    if resnet_size < 50:
      block_fn = resnet_model.building_block
      final_size = 512
    else:
      block_fn = resnet_model.bottleneck_block
      final_size = 2048

    super(ImagenetModel, self).__init__(
        resnet_size=resnet_size,
        num_classes=_NUM_CLASSES,
        num_filters=64,
        kernel_size=7,
        conv_stride=2,
        first_pool_size=3,
        first_pool_stride=2,
        second_pool_size=7,
        second_pool_stride=1,
        block_fn=block_fn,
        block_sizes=_get_block_sizes(resnet_size),
        block_strides=[1, 2, 2, 2],
        final_size=final_size,
        data_format=data_format)


def _get_block_sizes(resnet_size):
  """The number of block layers used for the Resnet model varies according
  to the size of the model. This helper grabs the layer set we want, throwing
  an error if a non-standard size has been selected.
  """
  choices = {
      18: [2, 2, 2, 2],
      34: [3, 4, 6, 3],
      50: [3, 4, 6, 3],
      101: [3, 4, 23, 3],
      152: [3, 8, 36, 3],
      200: [3, 24, 36, 3]
  }

  try:
    return choices[resnet_size]
  except KeyError:
    err = ('Could not find layers for selected Resnet size.\n'
           'Size received: {}; sizes allowed: {}.'.format(
               resnet_size, choices.keys()))
    raise ValueError(err)


def imagenet_model_fn(features, labels, mode, params):
  """Our model_fn for ResNet to be used with our Estimator."""
  learning_rate_fn = resnet_shared.learning_rate_with_decay(
      batch_size=params['batch_size'], batch_denom=256,
      num_images=_NUM_IMAGES['train'], boundary_epochs=[30, 60, 80, 90],
      decay_rates=[1, 0.1, 0.01, 0.001, 1e-4])

  return resnet_shared.resnet_model_fn(features, labels, mode, ImagenetModel,
                                       resnet_size=params['resnet_size'],
                                       weight_decay=1e-4,
                                       learning_rate_fn=learning_rate_fn,
                                       momentum=0.9,
                                       data_format=params['data_format'],
                                       loss_filter_fn=None)


def main(unused_argv):
  resnet_shared.resnet_main(FLAGS, imagenet_model_fn, input_fn)


if __name__ == '__main__':
  tf.logging.set_verbosity(tf.logging.INFO)

  parser = resnet_shared.ResnetArgParser(
      resnet_size_choices=[18, 34, 50, 101, 152, 200])
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(argv=[sys.argv[0]] + unparsed)