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99 lines
3.1 KiB
99 lines
3.1 KiB
5 years ago
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Provides data for the Cifar10 dataset.
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The dataset scripts used to create the dataset can be found at:
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tensorflow/models/slim/data/create_cifar10_dataset.py
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import tensorflow as tf
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from datasets import dataset_utils
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slim = tf.contrib.slim
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_FILE_PATTERN = 'cifar10_%s.tfrecord'
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SPLITS_TO_SIZES = {'train': 50000, 'test': 10000}
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_NUM_CLASSES = 10
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_ITEMS_TO_DESCRIPTIONS = {
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'image': 'A [32 x 32 x 3] color image.',
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'label': 'A single integer between 0 and 9',
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}
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def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
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"""Gets a dataset tuple with instructions for reading cifar10.
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Args:
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split_name: A train/test split name.
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dataset_dir: The base directory of the dataset sources.
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file_pattern: The file pattern to use when matching the dataset sources.
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It is assumed that the pattern contains a '%s' string so that the split
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name can be inserted.
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reader: The TensorFlow reader type.
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Returns:
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A `Dataset` namedtuple.
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Raises:
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ValueError: if `split_name` is not a valid train/test split.
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"""
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if split_name not in SPLITS_TO_SIZES:
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raise ValueError('split name %s was not recognized.' % split_name)
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if not file_pattern:
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file_pattern = _FILE_PATTERN
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file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
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# Allowing None in the signature so that dataset_factory can use the default.
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if not reader:
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reader = tf.TFRecordReader
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keys_to_features = {
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'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
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'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),
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'image/class/label': tf.FixedLenFeature(
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[], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
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}
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items_to_handlers = {
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'image': slim.tfexample_decoder.Image(shape=[32, 32, 3]),
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'label': slim.tfexample_decoder.Tensor('image/class/label'),
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}
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decoder = slim.tfexample_decoder.TFExampleDecoder(
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keys_to_features, items_to_handlers)
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labels_to_names = None
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if dataset_utils.has_labels(dataset_dir):
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labels_to_names = dataset_utils.read_label_file(dataset_dir)
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return slim.dataset.Dataset(
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data_sources=file_pattern,
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reader=reader,
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decoder=decoder,
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num_samples=SPLITS_TO_SIZES[split_name],
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items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
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num_classes=_NUM_CLASSES,
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labels_to_names=labels_to_names)
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