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
import vgg_preprocessing

_DEFAULT_IMAGE_SIZE = 224
_NUM_CHANNELS = 3
_NUM_CLASSES = 1001

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

_NUM_TRAIN_FILES = 1024
_SHUFFLE_BUFFER = 1500


###############################################################################
# Data processing
###############################################################################
def get_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(_NUM_TRAIN_FILES)]
  else:
    return [
        os.path.join(data_dir, 'validation-%05d-of-00128' % i)
        for i in range(128)]


def _parse_example_proto(example_serialized):
  """Parses an Example proto containing a training example of an image.

  The dataset contains serialized Example protocol buffers.
  The Example proto is expected to contain features named
  image/encoded (a JPEG-encoded string) and image/class/label (int)

  Args:
    example_serialized: scalar Tensor tf.string containing a serialized
      Example protocol buffer.

  Returns:
    image_buffer: Tensor tf.string containing the contents of a JPEG file.
    label: Tensor tf.int64 containing the label.
  """
  # Dense features in Example proto.
  feature_map = {
      'image/encoded': tf.FixedLenFeature([], dtype=tf.string,
                                          default_value=''),
      'image/class/label': tf.FixedLenFeature([1], dtype=tf.int64,
                                              default_value=-1)
  }

  features = tf.parse_single_example(example_serialized, feature_map)

  return features['image/encoded'], features['image/class/label']


def parse_record(raw_record, is_training):
  """Parses a record containing a training example of an image.

  The input record is parsed into a label and image, and the image is passed
  through preprocessing steps (cropping, flipping, and so on).

  Args:
    raw_record: scalar Tensor tf.string containing a serialized
      Example protocol buffer.
    is_training: A boolean denoting whether the input is for training.

  Returns:
    Tuple with processed image tensor and one-hot-encoded label tensor.
"""
  image, label = _parse_example_proto(raw_record)

  # Decode the string as an RGB JPEG.
  # Note that the resulting image contains an unknown height and width
  # that is set dynamically by decode_jpeg. In other words, the height
  # and width of image is unknown at compile-time.
  # Results in a 3-D int8 Tensor. This will be converted to a float later,
  # during resizing.
  image = tf.image.decode_jpeg(image, channels=_NUM_CHANNELS)

  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(label, shape=[]), dtype=tf.int32)
  label = tf.one_hot(label, _NUM_CLASSES)

  return image, label


def input_fn(is_training, data_dir, batch_size, num_epochs=1,
             num_parallel_calls=1, multi_gpu=False):
  """Input function which provides batches for train or eval.
  Args:
    is_training: A boolean denoting whether the input is for training.
    data_dir: The directory containing the input data.
    batch_size: The number of samples per batch.
    num_epochs: The number of epochs to repeat the dataset.
    num_parallel_calls: The number of records that are processed in parallel.
      This can be optimized per data set but for generally homogeneous data
      sets, should be approximately the number of available CPU cores.
    multi_gpu: Whether this is run multi-GPU. Note that this is only required
      currently to handle the batch leftovers, and can be removed
      when that is handled directly by Estimator.

  Returns:
    A dataset that can be used for iteration.
  """
  filenames = get_filenames(is_training, data_dir)
  dataset = tf.data.Dataset.from_tensor_slices(filenames)

  if is_training:
    # Shuffle the input files
    dataset = dataset.shuffle(buffer_size=_NUM_TRAIN_FILES)

  num_images = is_training and _NUM_IMAGES['train'] or _NUM_IMAGES['validation']

  # Convert to individual records
  dataset = dataset.flat_map(tf.data.TFRecordDataset)

  return resnet.process_record_dataset(
      dataset, is_training, batch_size, _SHUFFLE_BUFFER, parse_record,
      num_epochs, num_parallel_calls, examples_per_epoch=num_images,
      multi_gpu=multi_gpu)


def get_synth_input_fn():
  return resnet.get_synth_input_fn(
        _DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS, _NUM_CLASSES)


###############################################################################
# Running the model
###############################################################################
class ImagenetModel(resnet.Model):

  def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES):
    """These are the parameters that work for Imagenet data.

    Args:
      resnet_size: The number of convolutional layers needed in the model.
      data_format: Either 'channels_first' or 'channels_last', specifying which
        data format to use when setting up the model.
      num_classes: The number of output classes needed from the model. This
        enables users to extend the same model to their own datasets.
    """

    # For bigger models, we want to use "bottleneck" layers
    if resnet_size < 50:
      block_fn = resnet.building_block
      final_size = 512
    else:
      block_fn = resnet.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.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.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,
                                multi_gpu=params['multi_gpu'])


def main(unused_argv):
  input_function = FLAGS.use_synthetic_data and get_synth_input_fn() or input_fn
  resnet.resnet_main(FLAGS, imagenet_model_fn, input_function)


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

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