TensorFlow-Models/slim/datasets/imagenet.py

# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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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.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""Provides data for the ImageNet ILSVRC 2012 Dataset plus some bounding boxes.

Some images have one or more bounding boxes associated with the label of the
image. See details here: http://image-net.org/download-bboxes

ImageNet is based upon WordNet 3.0. To uniquely identify a synset, we use
"WordNet ID" (wnid), which is a concatenation of POS ( i.e. part of speech )
and SYNSET OFFSET of WordNet. For more information, please refer to the
WordNet documentation[http://wordnet.princeton.edu/wordnet/documentation/].

"There are bounding boxes for over 3000 popular synsets available.
For each synset, there are on average 150 images with bounding boxes."

WARNING: Don't use for object detection, in this case all the bounding boxes
of the image belong to just one class.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
from six.moves import urllib
import tensorflow as tf

from datasets import dataset_utils

slim = tf.contrib.slim

# TODO(nsilberman): Add tfrecord file type once the script is updated.
_FILE_PATTERN = '%s-*'

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

_ITEMS_TO_DESCRIPTIONS = {
    'image': 'A color image of varying height and width.',
    'label': 'The label id of the image, integer between 0 and 999',
    'label_text': 'The text of the label.',
    'object/bbox': 'A list of bounding boxes.',
    'object/label': 'A list of labels, one per each object.',
}

_NUM_CLASSES = 1001


def create_readable_names_for_imagenet_labels():
  """Create a dict mapping label id to human readable string.

  Returns:
      labels_to_names: dictionary where keys are integers from to 1000
      and values are human-readable names.

  We retrieve a synset file, which contains a list of valid synset labels used
  by ILSVRC competition. There is one synset one per line, eg.
          #   n01440764
          #   n01443537
  We also retrieve a synset_to_human_file, which contains a mapping from synsets
  to human-readable names for every synset in Imagenet. These are stored in a
  tsv format, as follows:
          #   n02119247    black fox
          #   n02119359    silver fox
  We assign each synset (in alphabetical order) an integer, starting from 1
  (since 0 is reserved for the background class).

  Code is based on
  https://github.com/tensorflow/models/blob/master/inception/inception/data/build_imagenet_data.py#L463
  """

  # pylint: disable=g-line-too-long
  base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/inception/inception/data/'
  synset_url = '{}/imagenet_lsvrc_2015_synsets.txt'.format(base_url)
  synset_to_human_url = '{}/imagenet_metadata.txt'.format(base_url)

  filename, _ = urllib.request.urlretrieve(synset_url)
  synset_list = [s.strip() for s in open(filename).readlines()]
  num_synsets_in_ilsvrc = len(synset_list)
  assert num_synsets_in_ilsvrc == 1000

  filename, _ = urllib.request.urlretrieve(synset_to_human_url)
  synset_to_human_list = open(filename).readlines()
  num_synsets_in_all_imagenet = len(synset_to_human_list)
  assert num_synsets_in_all_imagenet == 21842

  synset_to_human = {}
  for s in synset_to_human_list:
    parts = s.strip().split('\t')
    assert len(parts) == 2
    synset = parts[0]
    human = parts[1]
    synset_to_human[synset] = human

  label_index = 1
  labels_to_names = {0: 'background'}
  for synset in synset_list:
    name = synset_to_human[synset]
    labels_to_names[label_index] = name
    label_index += 1

  return labels_to_names


def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
  """Gets a dataset tuple with instructions for reading ImageNet.

  Args:
    split_name: A train/test split name.
    dataset_dir: The base directory of the dataset sources.
    file_pattern: The file pattern to use when matching the dataset sources.
      It is assumed that the pattern contains a '%s' string so that the split
      name can be inserted.
    reader: The TensorFlow reader type.

  Returns:
    A `Dataset` namedtuple.

  Raises:
    ValueError: if `split_name` is not a valid train/test split.
  """
  if split_name not in _SPLITS_TO_SIZES:
    raise ValueError('split name %s was not recognized.' % split_name)

  if not file_pattern:
    file_pattern = _FILE_PATTERN
  file_pattern = os.path.join(dataset_dir, file_pattern % split_name)

  # Allowing None in the signature so that dataset_factory can use the default.
  if reader is None:
    reader = tf.TFRecordReader

  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),
  }

  items_to_handlers = {
      'image': slim.tfexample_decoder.Image('image/encoded', 'image/format'),
      'label': slim.tfexample_decoder.Tensor('image/class/label'),
      'label_text': slim.tfexample_decoder.Tensor('image/class/text'),
      'object/bbox': slim.tfexample_decoder.BoundingBox(
          ['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'),
      'object/label': slim.tfexample_decoder.Tensor('image/object/class/label'),
  }

  decoder = slim.tfexample_decoder.TFExampleDecoder(
      keys_to_features, items_to_handlers)

  labels_to_names = None
  if dataset_utils.has_labels(dataset_dir):
    labels_to_names = dataset_utils.read_label_file(dataset_dir)
  else:
    labels_to_names = create_readable_names_for_imagenet_labels()
    dataset_utils.write_label_file(labels_to_names, dataset_dir)

  return slim.dataset.Dataset(
      data_sources=file_pattern,
      reader=reader,
      decoder=decoder,
      num_samples=_SPLITS_TO_SIZES[split_name],
      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
      num_classes=_NUM_CLASSES,
      labels_to_names=labels_to_names)