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# Copyright 2018 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.
# ==============================================================================
"""Efficient ImageNet input pipeline using tf.data.Dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
import resnet_preprocessing
class ImageNetInput(object):
def __init__(self,
width,
height,
resize,
is_training,
data_dir,
use_bfloat16=False,
per_core_batch_size=128):
self.image_preprocessing_fn = resnet_preprocessing.preprocess_image
self.is_training = is_training
self.width = width
self.height = height
self.resize = resize
self.use_bfloat16 = use_bfloat16
self.data_dir = data_dir
if self.data_dir == 'null' or self.data_dir == '':
self.data_dir = None
self.per_core_batch_size = per_core_batch_size
def dataset_parser(self, value):
"""Parse an ImageNet record from a serialized string Tensor."""
keys_to_features = {
'image/encoded':
tf.FixedLenFeature((), tf.string, ''),
'image/format':
tf.FixedLenFeature((), tf.string, 'png'),
'image/class/label':
tf.FixedLenFeature([], tf.int64, -1),
'image/height':
tf.FixedLenFeature([], tf.int64, -2),
'image/width':
tf.FixedLenFeature([], tf.int64, -3),
}
parsed = tf.parse_single_example(value, keys_to_features)
image_bytes = tf.reshape(parsed['image/encoded'], shape=[])
image = self.image_preprocessing_fn(
image_bytes,
width=self.width, height=self.height,
resize=self.resize,
is_training=self.is_training,
use_bfloat16=self.use_bfloat16,
)
# Subtract one so that labels are in [0, 1000), and cast to float32 for
# Keras model.
label = tf.cast(tf.cast(
tf.reshape(parsed['image/class/label'], shape=[1]), dtype=tf.int32), # - 1,
dtype=tf.float32)
return image, label
def input_fn(self):
"""Input function which provides a single batch for train or eval.
Returns:
A `tf.data.Dataset` object.
"""
# Shuffle the filenames to ensure better randomization.
file_pattern = os.path.join(
self.data_dir, 'websites_train*' if self.is_training else 'websites_validation*')
dataset = tf.data.Dataset.list_files(file_pattern, shuffle=self.is_training)
if self.is_training:
dataset = dataset.repeat()
def fetch_dataset(filename):
buffer_size = 100 * 1024 * 1024 # 100 MiB per file
dataset = tf.data.TFRecordDataset(filename, buffer_size=buffer_size)
return dataset
# Read the data from disk in parallel
dataset = dataset.interleave(fetch_dataset, cycle_length=16)
if self.is_training:
dataset = dataset.shuffle(1024)
# Parse, pre-process, and batch the data in parallel
dataset = dataset.apply(
tf.data.experimental.map_and_batch(
self.dataset_parser,
batch_size=self.per_core_batch_size,
num_parallel_batches=2,
drop_remainder=True))
# Prefetch overlaps in-feed with training
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
return dataset
# TODO(xiejw): Remove this generator when we have support for top_k
# evaluation.
def evaluation_generator(self, sess):
"""Creates a generator for evaluation."""
next_batch = self.input_fn().make_one_shot_iterator().get_next()
while True:
try:
yield sess.run(next_batch)
except tf.errors.OutOfRangeError:
return
def input_fn_null(self):
"""Input function which provides null (black) images."""
dataset = tf.data.Dataset.range(1).repeat().map(self._get_null_input)
dataset = dataset.prefetch(self.per_core_batch_size)
dataset = dataset.batch(self.per_core_batch_size, drop_remainder=True)
dataset = dataset.prefetch(32) # Prefetch overlaps in-feed with training
tf.logging.info('Input dataset: %s', str(dataset))
return dataset
def _get_null_input(self, _):
null_image = tf.zeros([320, 240, 3], tf.float32)
return null_image, tf.constant(0, tf.float32)
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