# Copyright 2016 Google Inc. 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.
# ==============================================================================
"""Read and preprocess image data.
Image processing occurs on a single image at a time. Image are read and
preprocessed in parallel across multiple threads. The resulting images
are concatenated together to form a single batch for training or evaluation.
-- Provide processed image data for a network:
inputs: Construct batches of evaluation examples of images.
distorted_inputs: Construct batches of training examples of images.
batch_inputs: Construct batches of training or evaluation examples of images.
-- Data processing:
parse_example_proto: Parses an Example proto containing a training example
of an image.
-- Image decoding:
decode_jpeg: Decode a JPEG encoded string into a 3-D float32 Tensor.
-- Image preprocessing:
image_preprocessing: Decode and preprocess one image for evaluation or training
distort_image: Distort one image for training a network.
eval_image: Prepare one image for evaluation.
distort_color: Distort the color in one image for training.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import inception.vgg_preprocessing as vgg_prep
import inception.lenet_preprocessing as lenet_prep
import re
import numpy as np
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('batch_size', 32,
"""Number of images to process in a batch.""")
tf.app.flags.DEFINE_integer('image_size', 224,
"""Provide square images of this size.""")
tf.app.flags.DEFINE_integer('num_preprocess_threads', 4,
"""Number of preprocessing threads per tower. """
"""Please make this a multiple of 4.""")
tf.app.flags.DEFINE_integer('num_readers', 4,
"""Number of parallel readers during train.""")
# Images are preprocessed asynchronously using multiple threads specified by
# --num_preprocss_threads and the resulting processed images are stored in a
# random shuffling queue. The shuffling queue dequeues --batch_size images
# for processing on a given Inception tower. A larger shuffling queue guarantees
# better mixing across examples within a batch and results in slightly higher
# predictive performance in a trained model. Empirically,
# --input_queue_memory_factor=16 works well. A value of 16 implies a queue size
# of 1024*16 images. Assuming RGB 299x299 images, this implies a queue size of
# 16GB. If the machine is memory limited, then decrease this factor to
# decrease the CPU memory footprint, accordingly.
tf.app.flags.DEFINE_integer('input_queue_memory_factor', 16,
"""Size of the queue of preprocessed images. """
"""Default is ideal but try smaller values, e.g. """
"""4, 2 or 1, if host memory is constrained. See """
"""comments in code for more details.""")
tf.app.flags.DEFINE_string('dataset_name','imagenet',
"""Dataset to train on (imagenet or cifar10).""")
_R_MEAN = 123.68
_G_MEAN = 116.78
_B_MEAN = 103.94
_RESIZE_SIDE = 256
def _mean_image_subtraction(image, means):
"""Subtracts the given means from each image channel.
For example:
means = [123.68, 116.779, 103.939]
image = _mean_image_subtraction(image, means)
Note that the rank of `image` must be known.
Args:
image: a tensor of size [height, width, C].
means: a C-vector of values to subtract from each channel.
Returns:
the centered image.
Raises:
ValueError: If the rank of `image` is unknown, if `image` has a rank other
than three or if the number of channels in `image` doesn't match the
number of values in `means`.
"""
if image.get_shape().ndims != 3:
raise ValueError('Input must be of size [height, width, C>0]')
num_channels = image.get_shape().as_list()[-1]
if len(means) != num_channels:
raise ValueError('len(means) must match the number of channels')
channels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)
for i in range(num_channels):
channels[i] -= means[i]
return tf.concat(axis=2, values=channels)
def inputs(dataset, batch_size=None, num_preprocess_threads=None):
"""Generate batches of ImageNet images for evaluation.
Use this function as the inputs for evaluating a network.
Note that some (minimal) image preprocessing occurs during evaluation
including central cropping and resizing of the image to fit the network.
Args:
dataset: instance of Dataset class specifying the dataset.
batch_size: integer, number of examples in batch
num_preprocess_threads: integer, total number of preprocessing threads but
None defaults to FLAGS.num_preprocess_threads.
Returns:
images: Images. 4D tensor of size [batch_size, FLAGS.image_size,
image_size, 3].
labels: 1-D integer Tensor of [FLAGS.batch_size].
"""
if not batch_size:
batch_size = FLAGS.batch_size
# Force all input processing onto CPU in order to reserve the GPU for
# the forward inference and back-propagation.
with tf.device('/cpu:0'):
images, labels = batch_inputs(
dataset, batch_size, train=False,
num_preprocess_threads=num_preprocess_threads,
num_readers=1)
return images, labels
def distorted_inputs(dataset, batch_size=None, num_preprocess_threads=None):
"""Generate batches of distorted versions of ImageNet images.
Use this function as the inputs for training a network.
Distorting images provides a useful technique for augmenting the data
set during training in order to make the network invariant to aspects
of the image that do not effect the label.
Args:
dataset: instance of Dataset class specifying the dataset.
batch_size: integer, number of examples in batch
num_preprocess_threads: integer, total number of preprocessing threads but
None defaults to FLAGS.num_preprocess_threads.
Returns:
images: Images. 4D tensor of size [batch_size, FLAGS.image_size,
FLAGS.image_size, 3].
labels: 1-D integer Tensor of [batch_size].
"""
if not batch_size:
batch_size = FLAGS.batch_size
# Force all input processing onto CPU in order to reserve the GPU for
# the forward inference and back-propagation.
with tf.device('/cpu:0'):
images, labels = batch_inputs(
dataset, batch_size, train=True,
num_preprocess_threads=num_preprocess_threads,
num_readers=FLAGS.num_readers)
return images, labels
def decode_raw(image_buffer, orig_height, orig_width, scope=None):
"""Decode a RAW string into one 3-D float image Tensor.
Args:
image_buffer: scalar string Tensor.
[orig_height, orig_width]: the size of original image
scope: Optional scope for op_scope.
Returns:
3-D float Tensor with values ranging from [0, 1).
"""
with tf.op_scope([image_buffer], scope, 'decode_raw'):
# Decode the string as an raw RGB.
image = tf.decode_raw(image_buffer, tf.uint8)
image = tf.reshape(image, tf.concat([orig_height,orig_width,[3]],0))
# After this point, all image pixels reside in [0,1)
# The various adjust_* ops all require this range for dtype float.
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
return image
def decode_jpeg(image_buffer, scope=None):
"""Decode a JPEG string into one 3-D float image Tensor.
Args:
image_buffer: scalar string Tensor.
scope: Optional scope for op_scope.
Returns:
3-D float Tensor with values ranging from [0, 1).
"""
with tf.op_scope([image_buffer], scope, 'decode_jpeg'):
# 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.
image = tf.image.decode_jpeg(image_buffer, channels=3)
# After this point, all image pixels reside in [0,1)
# until the very end, when they're rescaled to (-1, 1). The various
# adjust_* ops all require this range for dtype float.
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
return image
def decode_png(image_buffer, scope=None):
"""Decode a JPEG string into one 3-D float image Tensor.
Args:
image_buffer: scalar string Tensor.
scope: Optional scope for op_scope.
Returns:
3-D float Tensor with values ranging from [0, 1).
"""
with tf.op_scope([image_buffer], scope, 'decode_png'):
# 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.
image = tf.image.decode_png(image_buffer, channels=0)
# After this point, all image pixels reside in [0,1)
# until the very end, when they're rescaled to (-1, 1). The various
# adjust_* ops all require this range for dtype float.
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
return image
def distort_color(image, thread_id=0, scope=None):
"""Distort the color of the image.
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a distinct ordering
of color ops for each preprocessing thread.
Args:
image: Tensor containing single image.
thread_id: preprocessing thread ID.
scope: Optional scope for op_scope.
Returns:
color-distorted image
"""
with tf.op_scope([image], scope, 'distort_color'):
color_ordering = thread_id % 2
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
elif color_ordering == 1:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
# The random_* ops do not necessarily clamp.
image = tf.clip_by_value(image, 0.0, 1.0)
return image
def distort_image(image, height, width, bbox, thread_id=0, scope=None, add_summary=False):
"""Distort one image for training a network.
Distorting images provides a useful technique for augmenting the data
set during training in order to make the network invariant to aspects
of the image that do not effect the label.
Args:
image: 3-D float Tensor of image
height: integer
width: integer
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
where each coordinate is [0, 1) and the coordinates are arranged
as [ymin, xmin, ymax, xmax].
thread_id: integer indicating the preprocessing thread.
scope: Optional scope for op_scope.
Returns:
3-D float Tensor of distorted image used for training.
"""
with tf.op_scope([image, height, width, bbox], scope, 'distort_image'):
# Each bounding box has shape [1, num_boxes, box coords] and
# the coordinates are ordered [ymin, xmin, ymax, xmax].
# Display the bounding box in the first thread only.
if (not thread_id) and add_summary:
image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
bbox)
tf.summary.image('image_with_bounding_boxes', image_with_box)
# A large fraction of image datasets contain a human-annotated bounding
# box delineating the region of the image containing the object of interest.
# We choose to create a new bounding box for the object which is a randomly
# distorted version of the human-annotated bounding box that obeys an allowed
# range of aspect ratios, sizes and overlap with the human-annotated
# bounding box. If no box is supplied, then we assume the bounding box is
# the entire image.
sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
tf.shape(image),
bounding_boxes=bbox,
min_object_covered=0.1,
aspect_ratio_range=[0.75, 1.33],
area_range=[0.05, 1.0],
max_attempts=100,
use_image_if_no_bounding_boxes=True)
bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box
if (not thread_id) and add_summary:
image_with_distorted_box = tf.image.draw_bounding_boxes(
tf.expand_dims(image, 0), distort_bbox)
#tf.image_summary('images_with_distorted_bounding_box',
tf.summary.image('images_with_distorted_bounding_box',
image_with_distorted_box)
# Crop the image to the specified bounding box.
distorted_image = tf.slice(image, bbox_begin, bbox_size)
# This resizing operation may distort the images because the aspect
# ratio is not respected. We select a resize method in a round robin
# fashion based on the thread number.
# Note that ResizeMethod contains 4 enumerated resizing methods.
resize_method = thread_id % 4
distorted_image = tf.image.resize_images(distorted_image, [height, width],
method=resize_method)
# Restore the shape since the dynamic slice based upon the bbox_size loses
# the third dimension.
distorted_image.set_shape([height, width, 3])
if (not thread_id) and add_summary:
#tf.image_summary('cropped_resized_image',
tf.summary.image('cropped_resized_image',
tf.expand_dims(distorted_image, 0))
# Randomly flip the image horizontally.
distorted_image = tf.image.random_flip_left_right(distorted_image)
# Randomly distort the colors.
distorted_image = distort_color(distorted_image, thread_id)
if (not thread_id) and add_summary:
tf.summary.image('final_distorted_image',
tf.expand_dims(distorted_image, 0))
distorted_image = tf.subtract(distorted_image, 0.5)
distorted_image = tf.multiply(distorted_image, 2.0)
return distorted_image
def distort_alexnet_image(image, height, width, bbox, thread_id=0, scope=None, add_summary=False):
"""Distort one image for training a network.
Distorting images provides a useful technique for augmenting the data
set during training in order to make the network invariant to aspects
of the image that do not effect the label.
Args:
image: 3-D float Tensor of image
height: integer
width: integer
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
where each coordinate is [0, 1) and the coordinates are arranged
as [ymin, xmin, ymax, xmax].
thread_id: integer indicating the preprocessing thread.
scope: Optional scope for op_scope.
Returns:
3-D float Tensor of distorted image used for training.
"""
with tf.op_scope([image, height, width, bbox], scope, 'distort_image'):
# Each bounding box has shape [1, num_boxes, box coords] and
# the coordinates are ordered [ymin, xmin, ymax, xmax].
if (not thread_id) and add_summary:
tf.summary.image('original_image', tf.expand_dims(image, 0))
# This resizing operation may distort the images because the aspect
# ratio is not respected. We select a resize method in a round robin
# fashion based on the thread number.
# Note that ResizeMethod contains 4 enumerated resizing methods.
#resize_method = thread_id % 4
distorted_image = tf.image.resize_images(image, [_RESIZE_SIDE, _RESIZE_SIDE])
if (not thread_id) and add_summary:
tf.summary.image('resized_image', tf.expand_dims(distorted_image, 0))
# crop
distorted_image = tf.random_crop(distorted_image, [height, width, 3])
if (not thread_id) and add_summary:
tf.summary.image('cropped_resized_image', tf.expand_dims(distorted_image, 0))
# Randomly flip the image horizontally.
distorted_image = tf.image.random_flip_left_right(distorted_image)
if (not thread_id) and add_summary:
tf.summary.image('final_distorted_image', tf.expand_dims(distorted_image, 0))
# scale and reduce mean
distorted_image = tf.multiply(distorted_image, 255.0)
distorted_image = _mean_image_subtraction(distorted_image, [_R_MEAN, _G_MEAN, _B_MEAN])
return distorted_image
def distort_cifar10_image(image, height, width, thread_id=0, scope=None):
"""Distort one image for training a network.
Distorting images provides a useful technique for augmenting the data
set during training in order to make the network invariant to aspects
of the image that do not effect the label.
Args:
image: 3-D float Tensor of image
height: integer
width: integer
thread_id: integer indicating the preprocessing thread.
scope: Optional scope for op_scope.
Returns:
3-D float Tensor of distorted image used for training.
"""
with tf.name_scope(scope, 'distort_image', [image, height, width]):
# Each bounding box has shape [1, num_boxes, box coords] and
# the coordinates are ordered [ymin, xmin, ymax, xmax].
# Display the image in the first thread only.
if not thread_id:
tf.summary.image('original_image', tf.expand_dims(image, 0))
distorted_image = tf.random_crop(image, [height, width, 3])
if not thread_id:
tf.summary.image('cropped_image',
tf.expand_dims(distorted_image, 0))
# Randomly flip the image horizontally.
distorted_image = tf.image.random_flip_left_right(distorted_image)
# Because these operations are not commutative, consider randomizing
# the order their operation.
distorted_image = tf.image.random_brightness(distorted_image,
max_delta=63./255.)
distorted_image = tf.image.random_contrast(distorted_image,
lower=0.2, upper=1.8)
# The random_* ops do not necessarily clamp.
# distorted_image = tf.clip_by_value(distorted_image, 0.0, 1.0)
if not thread_id:
tf.summary.image('final_distorted_image',
tf.expand_dims(distorted_image, 0))
# Subtract off the mean and divide by the variance of the pixels.
distorted_image = tf.image.per_image_standardization(distorted_image)
distorted_image.set_shape([height, width, 3])
return distorted_image
def eval_image(image, height, width, scope=None):
"""Prepare one image for evaluation.
Args:
image: 3-D float Tensor
height: integer
width: integer
scope: Optional scope for op_scope.
Returns:
3-D float Tensor of prepared image.
"""
with tf.op_scope([image, height, width], scope, 'eval_image'):
# Crop the central region of the image with an area containing 87.5% of
# the original image.
image = tf.image.central_crop(image, central_fraction=0.875)
# Resize the image to the original height and width.
image = tf.expand_dims(image, 0)
image = tf.image.resize_bilinear(image, [height, width],
align_corners=False)
image = tf.squeeze(image, [0])
image = tf.subtract(image, 0.5)
image = tf.multiply(image, 2.0)
image.set_shape([height, width, 3])
return image
def eval_cifar10_image(image, height, width, scope=None):
"""Prepare one image for evaluation.
Args:
image: 3-D float Tensor
height: integer
width: integer
scope: Optional scope for op_scope.
Returns:
3-D float Tensor of prepared image.
"""
with tf.name_scope(scope, 'eval_image', [image, height, width]):
# Image processing for evaluation.
# Crop the central [height, width] of the image.
image = tf.image.resize_image_with_crop_or_pad(image, height, width)
# Subtract off the mean and divide by the variance of the pixels.
image = tf.image.per_image_standardization(image)
image.set_shape([height, width, 3])
return image
def eval_alexnet_image(image, height, width, scope=None):
"""Prepare one image for evaluation.
Args:
image: 3-D float Tensor
height: integer
width: integer
scope: Optional scope for op_scope.
Returns:
3-D float Tensor of prepared image.
"""
with tf.op_scope([image, height, width], scope, 'eval_image'):
image = tf.image.resize_images(image, [_RESIZE_SIDE, _RESIZE_SIDE])
# Crop the central region of the image
image = tf.image.resize_image_with_crop_or_pad(image, height, width)
# scale and reduce mean
image = tf.multiply(image, 255.0)
image.set_shape([height, width, 3])
image = _mean_image_subtraction(image, [_R_MEAN, _G_MEAN, _B_MEAN])
return image
def image_preprocessing(image_buffer, bbox, train, thread_id=0, image_format='JPEG',
orig_height=-1, orig_width=-1, add_summary=False):
"""Decode and preprocess one image for evaluation or training.
Args:
image_buffer: JPEG encoded string Tensor
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
where each coordinate is [0, 1) and the coordinates are arranged as
[ymin, xmin, ymax, xmax].
train: boolean
thread_id: integer indicating preprocessing thread
Returns:
3-D float Tensor containing an appropriately scaled image
Raises:
ValueError: if user does not provide bounding box
"""
if bbox is None:
raise ValueError('Please supply a bounding box.')
image = tf.cond(tf.equal('png', image_format),
lambda: decode_png(image_buffer),
lambda: tf.cond(tf.equal('RAW',image_format),
lambda: decode_raw(image_buffer, orig_height, orig_width),
lambda: decode_jpeg(image_buffer)
))
height = FLAGS.image_size
width = FLAGS.image_size
if train:
if FLAGS.dataset_name == 'cifar10':
image = distort_cifar10_image(image, height, width, thread_id)
elif FLAGS.dataset_name == 'imagenet':
if FLAGS.net == 'alexnet':
image = distort_alexnet_image(image, height, width, bbox, thread_id=thread_id, add_summary=add_summary)
elif re.compile('^vgg.*').match(FLAGS.net):
image = vgg_prep.preprocess_image(image, height, width, is_training=True)
else:
image = distort_image(image, height, width, bbox, thread_id=thread_id, add_summary=add_summary)
elif FLAGS.dataset_name == 'mnist':
image = lenet_prep.preprocess_image(image, height, width, is_training=True)
else:
raise ValueError("Wrong dataset_name!")
else:
if FLAGS.dataset_name == 'cifar10':
image = eval_cifar10_image(image, height, width)
elif FLAGS.dataset_name == 'imagenet':
if FLAGS.net == 'alexnet':
image = eval_alexnet_image(image, height, width)
elif re.compile('^vgg.*').match(FLAGS.net):
image = vgg_prep.preprocess_image(image, height, width, is_training=False)
else:
image = eval_image(image, height, width)
elif FLAGS.dataset_name == 'mnist':
image = lenet_prep.preprocess_image(image, height, width, is_training=False)
else:
raise ValueError("Wrong dataset_name!")
return image
def parse_example_proto(example_serialized):
"""Parses an Example proto containing a training example of an image.
The output of the build_image_data.py image preprocessing script is a dataset
containing serialized Example protocol buffers. Each Example proto contains
the following fields:
image/height: 462
image/width: 581
image/colorspace: 'RGB'
image/channels: 3
image/class/label: 615
image/class/synset: 'n03623198'
image/class/text: 'knee pad'
image/object/bbox/xmin: 0.1
image/object/bbox/xmax: 0.9
image/object/bbox/ymin: 0.2
image/object/bbox/ymax: 0.6
image/object/bbox/label: 615
image/format: 'JPEG'
image/filename: 'ILSVRC2012_val_00041207.JPEG'
image/encoded: <JPEG encoded string>
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.int32 containing the label.
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
where each coordinate is [0, 1) and the coordinates are arranged as
[ymin, xmin, ymax, xmax].
text: Tensor tf.string containing the human-readable label.
"""
# Dense features in Example proto.
feature_map = {
'image/height': tf.FixedLenFeature([1], dtype=tf.int64,
default_value=-1),
'image/width': tf.FixedLenFeature([1], dtype=tf.int64,
default_value=-1),
'image/channels': tf.FixedLenFeature([1], dtype=tf.int64,
default_value=-1),
'image/encoded': tf.FixedLenFeature([], dtype=tf.string,
default_value=''),
'image/class/label': tf.FixedLenFeature([1], dtype=tf.int64,
default_value=-1),
'image/class/text': tf.FixedLenFeature([], dtype=tf.string,
default_value=''),
'image/format': tf.FixedLenFeature([], dtype=tf.string,
default_value=''),
}
sparse_float32 = tf.VarLenFeature(dtype=tf.float32)
# Sparse features in Example proto.
feature_map.update(
{k: sparse_float32 for k in ['image/object/bbox/xmin',
'image/object/bbox/ymin',
'image/object/bbox/xmax',
'image/object/bbox/ymax']})
features = tf.parse_single_example(example_serialized, feature_map)
label = tf.cast(features['image/class/label'], dtype=tf.int32)
height = tf.cast(features['image/height'], dtype=tf.int32)
width = tf.cast(features['image/width'], dtype=tf.int32)
xmin = tf.expand_dims(features['image/object/bbox/xmin'].values, 0)
ymin = tf.expand_dims(features['image/object/bbox/ymin'].values, 0)
xmax = tf.expand_dims(features['image/object/bbox/xmax'].values, 0)
ymax = tf.expand_dims(features['image/object/bbox/ymax'].values, 0)
# Note that we impose an ordering of (y, x) just to make life difficult.
bbox = tf.concat([ymin, xmin, ymax, xmax], 0)
# Force the variable number of bounding boxes into the shape
# [1, num_boxes, coords].
bbox = tf.expand_dims(bbox, 0)
bbox = tf.transpose(bbox, [0, 2, 1])
return features['image/encoded'], label, bbox, features['image/class/text'], features['image/format'], height, width
def batch_inputs(dataset, batch_size, train, num_preprocess_threads=None,
num_readers=1, add_summary=False):
"""Contruct batches of training or evaluation examples from the image dataset.
Args:
dataset: instance of Dataset class specifying the dataset.
See dataset.py for details.
batch_size: integer
train: boolean
num_preprocess_threads: integer, total number of preprocessing threads
num_readers: integer, number of parallel readers
Returns:
images: 4-D float Tensor of a batch of images
labels: 1-D integer Tensor of [batch_size].
Raises:
ValueError: if data is not found
"""
with tf.name_scope('batch_processing'):
data_files = dataset.data_files()
if data_files is None:
raise ValueError('No data files found for this dataset')
# Create filename_queue
if train:
filename_queue = tf.train.string_input_producer(data_files,
shuffle=True,
capacity=16)
else:
filename_queue = tf.train.string_input_producer(data_files,
shuffle=False,
capacity=1)
if num_preprocess_threads is None:
num_preprocess_threads = FLAGS.num_preprocess_threads
if num_preprocess_threads % 4:
raise ValueError('Please make num_preprocess_threads a multiple '
'of 4 (%d % 4 != 0).', num_preprocess_threads)
if num_readers is None:
num_readers = FLAGS.num_readers
if num_readers < 1:
raise ValueError('Please make num_readers at least 1')
# Approximate number of examples per shard.
examples_per_shard = 1024
# Size the random shuffle queue to balance between good global
# mixing (more examples) and memory use (fewer examples).
# 1 image uses 299*299*3*4 bytes = 1MB
# The default input_queue_memory_factor is 16 implying a shuffling queue
# size: examples_per_shard * 16 * 1MB = 17.6GB
min_queue_examples = examples_per_shard * FLAGS.input_queue_memory_factor
if train:
examples_queue = tf.RandomShuffleQueue(
capacity=min_queue_examples + 3 * batch_size,
min_after_dequeue=min_queue_examples,
dtypes=[tf.string])
else:
examples_queue = tf.FIFOQueue(
capacity=examples_per_shard + 3 * batch_size,
dtypes=[tf.string])
# Create multiple readers to populate the queue of examples.
if num_readers > 1:
enqueue_ops = []
for _ in range(num_readers):
reader = dataset.reader()
_, value = reader.read(filename_queue)
enqueue_ops.append(examples_queue.enqueue([value]))
tf.train.queue_runner.add_queue_runner(
tf.train.queue_runner.QueueRunner(examples_queue, enqueue_ops))
example_serialized = examples_queue.dequeue()
else:
reader = dataset.reader()
_, example_serialized = reader.read(filename_queue)
images_and_labels = []
for thread_id in range(num_preprocess_threads):
# Parse a serialized Example proto to extract the image and metadata.
image_buffer, label_index, bbox, _, image_format, orig_height, orig_width = parse_example_proto(
example_serialized)
image = image_preprocessing(image_buffer, bbox, train, thread_id,
image_format=image_format, orig_height=orig_height, orig_width=orig_width, add_summary=add_summary)
images_and_labels.append([image, label_index])
images, label_index_batch = tf.train.batch_join(
images_and_labels,
batch_size=batch_size,
capacity=2 * num_preprocess_threads * batch_size)
# Reshape images into these desired dimensions.
height = FLAGS.image_size
width = FLAGS.image_size
if FLAGS.dataset_name == 'mnist':
depth = 1
else:
depth = 3
images = tf.cast(images, tf.float32)
images = tf.reshape(images, shape=[batch_size, height, width, depth])
# Display the training images in the visualizer.
if add_summary:
tf.summary.image('images', images)
return images, tf.reshape(label_index_batch, [batch_size])
