一、TFR数据读取
在train_ssd_network.py获取数据操作如下，首先需要slim.dataset.Dataset对象
# Select the dataset.
# 'imagenet', 'train', tfr文件存储位置
# TFR文件命名格式：'voc_2012_%s_*.tfrecord'，%s使用train或者test
dataset = dataset_factory.get_dataset(
FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)
获取过程会经过一系列臃肿的调用，我把中间被调用的函数（们）写在了下面，由上到下依次调用：
def get_dataset(name, split_name, dataset_dir, file_pattern=None, reader=None):
"""
Returns:
A `Dataset` class.
Raises:
ValueError: If the dataset `name` is unknown.
"""
if name not in datasets_map:
raise ValueError('Name of dataset unknown %s' % name)
# pascalvoc_2012.get_split
return datasets_map[name].get_split(split_name,
dataset_dir,
file_pattern,
reader)
def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
"""
Returns:
A `Dataset` namedtuple.
Raises:
ValueError: if `split_name` is not a valid train/test split.
"""
if not file_pattern:
file_pattern = FILE_PATTERN # 需要文件命名格式满足：'voc_2012_%s_*.tfrecord'
return pascalvoc_common.get_split(split_name, dataset_dir,
file_pattern, reader,
SPLITS_TO_SIZES, # {'train': 17125,}
ITEMS_TO_DESCRIPTIONS,
NUM_CLASSES # 20
)
"""
ITEMS_TO_DESCRIPTIONS = {
'image': 'A color image of varying height and width.',
'shape': 'Shape of the image',
'object/bbox': 'A list of bounding boxes, one per each object.',
'object/label': 'A list of labels, one per each object.',
}
"""
最终调用，获取slim.dataset.Dataset（解析见『TensorFlow』从磁盘读取数据），实际上能够传入满足slim.dataset.Dataset的参数即可：
def get_split(split_name, dataset_dir, file_pattern, reader,
split_to_sizes, items_to_descriptions, num_classes):
"""Gets a dataset tuple with instructions for reading Pascal VOC dataset.
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.
"""
# 'train'
if split_name not in split_to_sizes:
raise ValueError('split name %s was not recognized.' % split_name)
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
# Features in Pascal VOC TFRecords.
keys_to_features = { # 解码TFR文件方式
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string, default_value='jpeg'),
'image/height': tf.FixedLenFeature([1], tf.int64),
'image/width': tf.FixedLenFeature([1], tf.int64),
'image/channels': tf.FixedLenFeature([1], tf.int64),
'image/shape': tf.FixedLenFeature([3], tf.int64),
'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/bbox/label': tf.VarLenFeature(dtype=tf.int64),
'image/object/bbox/difficult': tf.VarLenFeature(dtype=tf.int64),
'image/object/bbox/truncated': tf.VarLenFeature(dtype=tf.int64),
}
items_to_handlers = { # 解码二进制数据条目
'image': slim.tfexample_decoder.Image('image/encoded', 'image/format'),
'shape': slim.tfexample_decoder.Tensor('image/shape'),
'object/bbox': slim.tfexample_decoder.BoundingBox(
['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'),
'object/label': slim.tfexample_decoder.Tensor('image/object/bbox/label'),
'object/difficult': slim.tfexample_decoder.Tensor('image/object/bbox/difficult'),
'object/truncated': slim.tfexample_decoder.Tensor('image/object/bbox/truncated'),
}
# 解码实施
decoder = slim.tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
labels_to_names = None
# tf.gfile.Exists(os.path.join(dataset_dir, 'labels.txt'))
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, # TFR文件名
reader=reader, # 阅读器
decoder=decoder, # 解码Tensor
num_samples=split_to_sizes[split_name], # 数目
items_to_descriptions=items_to_descriptions, # decoder条目描述字段
num_classes=num_classes, # 类别数
labels_to_names=labels_to_names # 字典{图片:类别,……}
)
''' items_to_descriptions:
{'image': 'A color image of varying height and width.',
'shape': 'Shape of the image',
'object/bbox': 'A list of bounding boxes, one per each object.',
'object/label': 'A list of labels, one per each object.',}
'''
下面从TFR中获取1 batch的数据：
with tf.name_scope(FLAGS.dataset_name + '_data_provider'):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset, # DatasetDataProvider 需要 slim.dataset.Dataset 做参数
num_readers=FLAGS.num_readers,
common_queue_capacity=20 * FLAGS.batch_size,
common_queue_min=10 * FLAGS.batch_size,
shuffle=True)
# Get for SSD network: image, labels, bboxes.c
# DatasetDataProvider可以通过TFR字段获取batch size数据
[image, shape, glabels, gbboxes] = provider.get(['image', 'shape',
'object/label',
'object/bbox'])
此时数据已经获取完毕，预处理之后即可加入运算。
注意，直到现在为止，我们仅对图片数据进行了解码，并没有扩充维度，也就是说其维度依然是3维。
二、数据处理
获取对应数据集的预处里函数，并使用其处理上面小结中获取的batch数据，
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
preprocessing_name, is_training=True)
# Pre-processing image, labels and bboxes.
image, glabels, gbboxes = \
image_preprocessing_fn(image, glabels, gbboxes,
out_shape=ssd_shape, # (300,300)
data_format=DATA_FORMAT) # 'NCHW'
有的时候你会觉得这种层层调用非常的sb……下面两步依旧是个调用链，
def get_preprocessing(name, is_training=False):
preprocessing_fn_map = {
'ssd_300_vgg': ssd_vgg_preprocessing,
'ssd_512_vgg': ssd_vgg_preprocessing,
}
if name not in preprocessing_fn_map:
raise ValueError('Preprocessing name [%s] was not recognized' % name)
def preprocessing_fn(image, labels, bboxes,
out_shape, data_format='NHWC', **kwargs):
return preprocessing_fn_map[name].preprocess_image(
image, labels, bboxes, out_shape, data_format=data_format,
is_training=is_training, **kwargs)
return preprocessing_fn
def preprocess_image(image,
labels,
bboxes,
out_shape,
data_format,
is_training=False,
**kwargs):
if is_training:
return preprocess_for_train(image, labels, bboxes,
out_shape=out_shape,
data_format=data_format)
else:
return preprocess_for_eval(image, labels, bboxes,
out_shape=out_shape,
data_format=data_format,
**kwargs)
之后就是数据具体的预处理函数，