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cifar10.py

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+# 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.
+# ==============================================================================
+"""CIFAR-10 data set.
+
+See http://www.cs.toronto.edu/~kriz/cifar.html.
+"""
+import os
+
+import tensorflow as tf
+
+HEIGHT = 32
+WIDTH = 32
+DEPTH = 3
+
+
+class Cifar10DataSet(object):
+  """Cifar10 data set.
+
+  Described by http://www.cs.toronto.edu/~kriz/cifar.html.
+  """
+
+  def __init__(self, data_dir, subset='train', use_distortion=True):
+    self.data_dir = data_dir
+    self.subset = subset
+    self.use_distortion = use_distortion
+
+  def get_filenames(self):
+    if self.subset in ['train', 'validation', 'eval']:
+      return [os.path.join(self.data_dir, self.subset + '.tfrecords')]
+    else:
+      raise ValueError('Invalid data subset "%s"' % self.subset)
+
+  def parser(self, serialized_example):
+    """Parses a single tf.Example into image and label tensors."""
+    # Dimensions of the images in the CIFAR-10 dataset.
+    # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the
+    # input format.
+    features = tf.parse_single_example(
+        serialized_example,
+        features={
+            'image': tf.FixedLenFeature([], tf.string),
+            'label': tf.FixedLenFeature([], tf.int64),
+        })
+    image = tf.decode_raw(features['image'], tf.uint8)
+    image.set_shape([DEPTH * HEIGHT * WIDTH])
+
+    # Reshape from [depth * height * width] to [depth, height, width].
+    image = tf.cast(
+        tf.transpose(tf.reshape(image, [DEPTH, HEIGHT, WIDTH]), [1, 2, 0]),
+        tf.float32)
+    label = tf.cast(features['label'], tf.int32)
+
+    # Custom preprocessing.
+    image = self.preprocess(image)
+
+    return image, label
+
+  def make_batch(self, batch_size):
+    """Read the images and labels from 'filenames'."""
+    filenames = self.get_filenames()
+    # Repeat infinitely.
+    dataset = tf.data.TFRecordDataset(filenames).repeat()
+
+    # Parse records.
+    dataset = dataset.map(
+        self.parser, num_parallel_calls=batch_size)
+
+    # Potentially shuffle records.
+    if self.subset == 'train':
+      min_queue_examples = int(
+          Cifar10DataSet.num_examples_per_epoch(self.subset) * 0.4)
+      # Ensure that the capacity is sufficiently large to provide good random
+      # shuffling.
+      dataset = dataset.shuffle(buffer_size=min_queue_examples + 3 * batch_size)
+
+    # Batch it up.
+    dataset = dataset.batch(batch_size)
+    iterator = dataset.make_one_shot_iterator()
+    image_batch, label_batch = iterator.get_next()
+
+    return image_batch, label_batch
+
+  def preprocess(self, image):
+    """Preprocess a single image in [height, width, depth] layout."""
+    if self.subset == 'train' and self.use_distortion:
+      # Pad 4 pixels on each dimension of feature map, done in mini-batch
+      image = tf.image.resize_image_with_crop_or_pad(image, 40, 40)
+      image = tf.random_crop(image, [HEIGHT, WIDTH, DEPTH])
+      image = tf.image.random_flip_left_right(image)
+    return image
+
+  @staticmethod
+  def num_examples_per_epoch(subset='train'):
+    if subset == 'train':
+      return 45000
+    elif subset == 'validation':
+      return 5000
+    elif subset == 'eval':
+      return 10000
+    else:
+      raise ValueError('Invalid data subset "%s"' % subset)