# =========================================================================== #
# SSD Network flags.
# =========================================================================== #
# localization框的衰減比率
tf.app.flags.DEFINE_float(
    'loss_alpha', 1., 'Alpha parameter in the loss function.')
# 正負(fù)樣本比率
tf.app.flags.DEFINE_float(
    'negative_ratio', 3., 'Negative ratio in the loss function.')
# ground truth處理后，匹配得分高于match_threshold屬于正樣本
tf.app.flags.DEFINE_float(
    'match_threshold', 0.5, 'Matching threshold in the loss function.')

# =========================================================================== #
# General Flags.
# =========================================================================== #
# train_dir用于保存訓(xùn)練后的模型和日志
tf.app.flags.DEFINE_string(
    'train_dir', '/tmp/tfmodel/',
    'Directory where checkpoints and event logs are written to.')
# num_readers是在對(duì)數(shù)據(jù)集進(jìn)行讀取時(shí)所用的平行讀取器個(gè)數(shù)
tf.app.flags.DEFINE_integer(
    'num_readers', 4,
    'The number of parallel readers that read data from the dataset.')
# 在進(jìn)行訓(xùn)練batch的構(gòu)建時(shí)，所用的線程數(shù)
tf.app.flags.DEFINE_integer(
    'num_preprocessing_threads', 4,
    'The number of threads used to create the batches.')
# 每十步進(jìn)行一次log輸出，在窗口上
tf.app.flags.DEFINE_integer(
    'log_every_n_steps', 10,
    'The frequency with which logs are print.')
# 每600秒存儲(chǔ)一次記錄
tf.app.flags.DEFINE_integer(
    'save_summaries_secs', 600,
    'The frequency with which summaries are saved, in seconds.')
# 每600秒存儲(chǔ)一次模型
tf.app.flags.DEFINE_integer(
    'save_interval_secs', 600,
    'The frequency with which the model is saved, in seconds.')
# 可以使用的gpu內(nèi)存數(shù)量
tf.app.flags.DEFINE_float(
    'gpu_memory_fraction', 0.7, 'GPU memory fraction to use.')

# =========================================================================== #
# Optimization Flags.
# =========================================================================== #
# 優(yōu)化器參數(shù)
# weight_decay參數(shù)
tf.app.flags.DEFINE_float(
    'weight_decay', 0.00004, 'The weight decay on the model weights.')
# 使用什么優(yōu)化器
tf.app.flags.DEFINE_string(
    'optimizer', 'rmsprop',
    'The name of the optimizer, one of "adadelta", "adagrad", "adam",'
    '"ftrl", "momentum", "sgd" or "rmsprop".')
tf.app.flags.DEFINE_float(
    'adadelta_rho', 0.95,
    'The decay rate for adadelta.')
tf.app.flags.DEFINE_float(
    'adagrad_initial_accumulator_value', 0.1,
    'Starting value for the AdaGrad accumulators.')
tf.app.flags.DEFINE_float(
    'adam_beta1', 0.9,
    'The exponential decay rate for the 1st moment estimates.')
tf.app.flags.DEFINE_float(
    'adam_beta2', 0.999,
    'The exponential decay rate for the 2nd moment estimates.')
tf.app.flags.DEFINE_float('opt_epsilon', 1.0, 'Epsilon term for the optimizer.')
tf.app.flags.DEFINE_float('ftrl_learning_rate_power', -0.5,
                          'The learning rate power.')
tf.app.flags.DEFINE_float(
    'ftrl_initial_accumulator_value', 0.1,
    'Starting value for the FTRL accumulators.')
tf.app.flags.DEFINE_float(
    'ftrl_l1', 0.0, 'The FTRL l1 regularization strength.')
tf.app.flags.DEFINE_float(
    'ftrl_l2', 0.0, 'The FTRL l2 regularization strength.')
tf.app.flags.DEFINE_float(
    'momentum', 0.9,
    'The momentum for the MomentumOptimizer and RMSPropOptimizer.')
tf.app.flags.DEFINE_float('rmsprop_momentum', 0.9, 'Momentum.')
tf.app.flags.DEFINE_float('rmsprop_decay', 0.9, 'Decay term for RMSProp.')

# =========================================================================== #
# Learning Rate Flags.
# =========================================================================== #
# 學(xué)習(xí)率衰減的方式，有固定、指數(shù)衰減等
tf.app.flags.DEFINE_string(
    'learning_rate_decay_type',
    'exponential',
    'Specifies how the learning rate is decayed. One of "fixed", "exponential",'
    ' or "polynomial"')
# 初始學(xué)習(xí)率
tf.app.flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
# 結(jié)束時(shí)的學(xué)習(xí)率
tf.app.flags.DEFINE_float(
    'end_learning_rate', 0.0001,
    'The minimal end learning rate used by a polynomial decay learning rate.')
tf.app.flags.DEFINE_float(
    'label_smoothing', 0.0, 'The amount of label smoothing.')
# 學(xué)習(xí)率衰減因素
tf.app.flags.DEFINE_float(
    'learning_rate_decay_factor', 0.94, 'Learning rate decay factor.')
tf.app.flags.DEFINE_float(
    'num_epochs_per_decay', 2.0,
    'Number of epochs after which learning rate decays.')
tf.app.flags.DEFINE_float(
    'moving_average_decay', None,
    'The decay to use for the moving average.'
    'If left as None, then moving averages are not used.')

# =========================================================================== #
# Dataset Flags.
# =========================================================================== #
# 數(shù)據(jù)集名稱
tf.app.flags.DEFINE_string(
    'dataset_name', 'imagenet', 'The name of the dataset to load.')
# 數(shù)據(jù)集種類(lèi)個(gè)數(shù)
tf.app.flags.DEFINE_integer(
    'num_classes', 21, 'Number of classes to use in the dataset.')
# 訓(xùn)練還是測(cè)試
tf.app.flags.DEFINE_string(
    'dataset_split_name', 'train', 'The name of the train/test split.')
# 數(shù)據(jù)集目錄
tf.app.flags.DEFINE_string(
    'dataset_dir', None, 'The directory where the dataset files are stored.')
tf.app.flags.DEFINE_integer(
    'labels_offset', 0,
    'An offset for the labels in the dataset. This flag is primarily used to '
    'evaluate the VGG and ResNet architectures which do not use a background '
    'class for the ImageNet dataset.')
tf.app.flags.DEFINE_string(
    'model_name', 'ssd_300_vgg', 'The name of the architecture to train.')
tf.app.flags.DEFINE_string(
    'preprocessing_name', None, 'The name of the preprocessing to use. If left '
    'as `None`, then the model_name flag is used.')
# 每一次訓(xùn)練batch的大小
tf.app.flags.DEFINE_integer(
    'batch_size', 32, 'The number of samples in each batch.')
# 訓(xùn)練圖片的大小
tf.app.flags.DEFINE_integer(
    'train_image_size', None, 'Train image size')
# 最大訓(xùn)練次數(shù)
tf.app.flags.DEFINE_integer('max_number_of_steps', 50000,
                            'The maximum number of training steps.')

# =========================================================================== #
# Fine-Tuning Flags.
# =========================================================================== #
# 該部分參數(shù)用于微修已有的模型
# 原模型的位置
tf.app.flags.DEFINE_string(
    'checkpoint_path', None,
    'The path to a checkpoint from which to fine-tune.')
tf.app.flags.DEFINE_string(
    'checkpoint_model_scope', None,
    'Model scope in the checkpoint. None if the same as the trained model.')
# 哪些變量不要
tf.app.flags.DEFINE_string(
    'checkpoint_exclude_scopes', None,
    'Comma-separated list of scopes of variables to exclude when restoring '
    'from a checkpoint.')
# 那些變量不訓(xùn)練
tf.app.flags.DEFINE_string(
    'trainable_scopes', None,
    'Comma-separated list of scopes to filter the set of variables to train.'
    'By default, None would train all the variables.')
# 忽略丟失的變量
tf.app.flags.DEFINE_boolean(
    'ignore_missing_vars', False,
    'When restoring a checkpoint would ignore missing variables.')
FLAGS = tf.app.flags.FLAGS

##########################讀取數(shù)據(jù)集部分#############################
# 選擇數(shù)據(jù)庫(kù)
dataset = dataset_factory.get_dataset(
    FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datasets import cifar10
from datasets import imagenet
from datasets import pascalvoc_2007
from datasets import pascalvoc_2012
datasets_map = {
    'cifar10': cifar10,
    'imagenet': imagenet,
    'pascalvoc_2007': pascalvoc_2007,
    'pascalvoc_2012': pascalvoc_2012,
}
def get_dataset(name, split_name, dataset_dir, file_pattern=None, reader=None):
    """
    給定一個(gè)數(shù)據(jù)集名和一個(gè)拆分名返回一個(gè)數(shù)據(jù)集。
    參數(shù):
        name: String, 數(shù)據(jù)集名稱
        split_name: 訓(xùn)練還是測(cè)試
        dataset_dir: 存儲(chǔ)數(shù)據(jù)集文件的目錄。
        file_pattern: 用于匹配數(shù)據(jù)集源文件的文件模式。
        reader: tf.readerbase的子類(lèi)。如果保留為“none”，則使用每個(gè)數(shù)據(jù)集定義的默認(rèn)讀取器。
    Returns:
        數(shù)據(jù)集
    """
    if name not in datasets_map:
        raise ValueError('Name of dataset unknown %s' % name)
    return datasets_map[name].get_split(split_name,
                                        dataset_dir,
                                        file_pattern,
                                        reader)

pascalvoc_2012.get_split(split_name,
						dataset_dir,
						file_pattern,
						reader)

def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
    """Gets a dataset tuple with instructions for reading ImageNet.
    Args:
      split_name: 訓(xùn)練還是測(cè)試
      dataset_dir: 數(shù)據(jù)集的位置
      file_pattern: 匹配數(shù)據(jù)集源時(shí)要使用的文件模式。
                    假定模式包含一個(gè)'%s'字符串，以便可以插入拆分名稱
      reader: TensorFlow閱讀器類(lèi)型。
    Returns:
      數(shù)據(jù)集.
    """
    if not file_pattern:
        file_pattern = FILE_PATTERN
    return pascalvoc_common.get_split(split_name, dataset_dir,
                                      file_pattern, reader,
                                      SPLITS_TO_SIZES,
                                      ITEMS_TO_DESCRIPTIONS,
                                      NUM_CLASSES)

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.
    給定一個(gè)數(shù)據(jù)集名和一個(gè)拆分名返回一個(gè)數(shù)據(jù)集。
    參數(shù):
        name: String, 數(shù)據(jù)集名稱
        split_name: 訓(xùn)練還是測(cè)試
        dataset_dir: 存儲(chǔ)數(shù)據(jù)集文件的目錄。
        file_pattern: 用于匹配數(shù)據(jù)集源文件的文件模式。
        reader: tf.readerbase的子類(lèi)。如果保留為“none”，則使用每個(gè)數(shù)據(jù)集定義的默認(rèn)讀取器。
    Returns:
        數(shù)據(jù)集
    """
    if split_name not in split_to_sizes:
        raise ValueError('split name %s was not recognized.' % split_name)
    # file_pattern是取得的tfrecord數(shù)據(jù)集的位置
    file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
    # 當(dāng)沒(méi)有的時(shí)候使用默認(rèn)reader
    if reader is None:
        reader = tf.TFRecordReader
    # VOC數(shù)據(jù)集中的文檔內(nèi)容
    keys_to_features = {
        '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),
    }
    # 解碼方式
    items_to_handlers = {
        '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'),
    }
    # 將tfrecord上keys_to_features的部分解碼到items_to_handlers上
    decoder = slim.tfexample_decoder.TFExampleDecoder(
        keys_to_features, items_to_handlers)
    labels_to_names = None
    if dataset_utils.has_labels(dataset_dir):
        labels_to_names = dataset_utils.read_label_file(dataset_dir)
    return slim.dataset.Dataset(
            data_sources=file_pattern,  # 數(shù)據(jù)源
            reader=reader,              # tf.TFRecordReader
            decoder=decoder,            # 解碼結(jié)果
            num_samples=split_to_sizes[split_name], # 17125
            items_to_descriptions=items_to_descriptions,    # 每一個(gè)item的描述
            num_classes=num_classes,                        # 種類(lèi)
            labels_to_names=labels_to_names)    

###########################建立ssd網(wǎng)絡(luò)##############################
# 獲得SSD的網(wǎng)絡(luò)和它的先驗(yàn)框
ssd_class = ssd_vgg_300.SSDNet
# 替換種類(lèi)的數(shù)量num_classes參數(shù)
ssd_params = ssd_class.default_params._replace(num_classes=FLAGS.num_classes)
# 成功建立了網(wǎng)絡(luò)net，替換參數(shù)
ssd_net = ssd_class(ssd_params)
# 獲得先驗(yàn)框
ssd_shape = ssd_net.params.img_shape
ssd_anchors = ssd_net.anchors(ssd_shape) # 包括六個(gè)特征層的先驗(yàn)框

###########################預(yù)處理數(shù)據(jù)集##############################
# preprocessing_name等于ssd_300_vgg
preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
# 根據(jù)名字進(jìn)行處理獲得處理函數(shù)
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
    preprocessing_name, is_training=True)
# 打印參數(shù)
tf_utils.print_configuration(FLAGS.__flags, ssd_params,
                                dataset.data_sources, FLAGS.train_dir)
# DatasetDataProviders從數(shù)據(jù)集中提供數(shù)據(jù). 通過(guò)配置，
# 可以同時(shí)使用多個(gè)readers或者使用單個(gè)reader提供數(shù)據(jù)。此外，被讀取的數(shù)據(jù)
# 可以被打亂順序
# 預(yù)加載
with tf.name_scope(FLAGS.dataset_name + '_data_provider'):
    provider = slim.dataset_data_provider.DatasetDataProvider(
        dataset,
        num_readers=FLAGS.num_readers,
        common_queue_capacity=20 * FLAGS.batch_size,
        common_queue_min=10 * FLAGS.batch_size,
        shuffle=True)
# 獲取原始的圖片和它對(duì)應(yīng)的label，框ground truth的位置
[image, _, glabels, gbboxes] = provider.get(['image', 'shape',
                                                    'object/label',
                                                    'object/bbox'])
# 預(yù)處理圖片標(biāo)簽和框的位置
image, glabels, gbboxes = \
    image_preprocessing_fn(image, glabels, gbboxes,
                            out_shape=ssd_shape,
                            data_format=DATA_FORMAT)

# 根據(jù)名字進(jìn)行處理獲得處理函數(shù)
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
    preprocessing_name, is_training=True)

def get_preprocessing(name, is_training=False):
    preprocessing_fn_map = {
        'ssd_300_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):
        # 這里實(shí)際上調(diào)用ssd_vgg_preprocessing.preprocess_image
        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):
    """Pre-process an given image.
    Args:
      image: A `Tensor` representing an image of arbitrary size.
      output_height: 預(yù)處理后圖像的高度。
      output_width: 預(yù)處理后圖像的寬度。
      is_training: 如果我們正在對(duì)圖像進(jìn)行預(yù)處理以進(jìn)行訓(xùn)練，則為true；否則為false
      resize_side_min: 圖像最小邊的下界，用于保持方向的大小調(diào)整，
                如果“is_training”為“false”，則此值
                用于重新縮放
      resize_side_max: 圖像最小邊的上界，用于保持方向的大小調(diào)整                 
                如果“is_training”為“false”，則此值
                用于重新縮放
                the resize side is sampled from 
                [resize_size_min, resize_size_max].
    Returns:
      預(yù)處理后的圖片
    """
    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)

def preprocess_for_train(image, labels, bboxes,
                         out_shape, data_format='NHWC',
                         scope='ssd_preprocessing_train'):
    """Preprocesses the given image for training.
    Note that the actual resizing scale is sampled from
        [`resize_size_min`, `resize_size_max`].
    參數(shù):
        image: 圖片，任意size的圖片.
        output_height: 處理后的圖片高度.
        output_width: 處理后的圖片寬度.
        resize_side_min: 圖像最小邊的下界，用于保方面調(diào)整大小
        resize_side_max: 圖像最小邊的上界，用于保方面調(diào)整大小
    Returns:
        處理過(guò)的圖片
    """
    fast_mode = False
    with tf.name_scope(scope, 'ssd_preprocessing_train', [image, labels, bboxes]):
        if image.get_shape().ndims != 3:
            raise ValueError('Input must be of size [height, width, C>0]')
        # 改變圖片的數(shù)據(jù)類(lèi)型
        if image.dtype != tf.float32:
            image = tf.image.convert_image_dtype(image, dtype=tf.float32)
        # 樣本框扭曲
        dst_image = image
        dst_image, labels, bboxes, _ = \
            distorted_bounding_box_crop(image, labels, bboxes,
                                        min_object_covered=MIN_OBJECT_COVERED,
                                        aspect_ratio_range=CROP_RATIO_RANGE)
        # 將圖像大小調(diào)整為輸出大小。
        dst_image = tf_image.resize_image(dst_image, out_shape,
                                          method=tf.image.ResizeMethod.BILINEAR,
                                          align_corners=False)
        # 隨機(jī)水平翻轉(zhuǎn)圖像.
        dst_image, bboxes = tf_image.random_flip_left_right(dst_image, bboxes)
        # 隨機(jī)扭曲顏色。有四種方法.
        dst_image = apply_with_random_selector(
                dst_image,
                lambda x, ordering: distort_color(x, ordering, fast_mode),
                num_cases=4)
        # 圖像減去平均值
        image = dst_image * 255.
        image = tf_image_whitened(image, [_R_MEAN, _G_MEAN, _B_MEAN])
        # 圖像的類(lèi)型
        if data_format == 'NCHW':
            image = tf.transpose(image, perm=(2, 0, 1))
        return image, labels, bboxes

gclasses, glocalisations, gscores = ssd_net.bboxes_encode(glabels, gbboxes, ssd_anchors)

def bboxes_encode(self, labels, bboxes, anchors,
                    scope=None):
    """
    進(jìn)行編碼操作
    """
    return ssd_common.tf_ssd_bboxes_encode(
        labels, bboxes, anchors,
        self.params.num_classes,
        self.params.no_annotation_label,
        ignore_threshold=0.5,
        prior_scaling=self.params.prior_scaling,
        scope=scope)

def tf_ssd_bboxes_encode(labels,
                         bboxes,
                         anchors,
                         num_classes,
                         no_annotation_label,
                         ignore_threshold=0.5,
                         prior_scaling=[0.1, 0.1, 0.2, 0.2],
                         dtype=tf.float32,
                         scope='ssd_bboxes_encode'):
    """
      對(duì)每一個(gè)特征層進(jìn)行解碼
    """
    with tf.name_scope(scope):
        target_labels = []
        target_localizations = []
        target_scores = []
        for i, anchors_layer in enumerate(anchors):
            with tf.name_scope('bboxes_encode_block_%i' % i):
                t_labels, t_loc, t_scores = \
                    tf_ssd_bboxes_encode_layer(labels, bboxes, anchors_layer,
                                               num_classes, no_annotation_label,
                                               ignore_threshold,
                                               prior_scaling, dtype)
                target_labels.append(t_labels)
                target_localizations.append(t_loc)
                target_scores.append(t_scores)
        return target_labels, target_localizations, target_scores

    yref, xref, href, wref = anchors_layer
    ymin = yref - href / 2.
    xmin = xref - wref / 2.
    ymax = yref + href / 2.
    xmax = xref + wref / 2.
    vol_anchors = (xmax - xmin) * (ymax - ymin)
    # 1、創(chuàng)建一系列變量存儲(chǔ)編碼結(jié)果
    # 每個(gè)特征層的shape
    shape = (yref.shape[0], yref.shape[1], href.size)
    # 每個(gè)特征層特定點(diǎn)，特定框的label
    feat_labels = tf.zeros(shape, dtype=tf.int64)  # (m, m, k)
    # 每個(gè)特征層特定點(diǎn)，特定框的得分
    feat_scores = tf.zeros(shape, dtype=dtype)
    # 每個(gè)特征層特定點(diǎn)，特定框的位置
    feat_ymin = tf.zeros(shape, dtype=dtype)
    feat_xmin = tf.zeros(shape, dtype=dtype)
    feat_ymax = tf.ones(shape, dtype=dtype)
    feat_xmax = tf.ones(shape, dtype=dtype)

    # 用于計(jì)算IOU
    def jaccard_with_anchors(bbox):
        int_ymin = tf.maximum(ymin, bbox[0])  # (m, m, k)
        int_xmin = tf.maximum(xmin, bbox[1])
        int_ymax = tf.minimum(ymax, bbox[2])
        int_xmax = tf.minimum(xmax, bbox[3])
        h = tf.maximum(int_ymax - int_ymin, 0.)
        w = tf.maximum(int_xmax - int_xmin, 0.)
        # Volumes.
        # 處理搜索框和bbox之間的聯(lián)系
        inter_vol = h * w  # 交集面積
        union_vol = vol_anchors - inter_vol \
                    + (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])  # 并集面積
        jaccard = tf.div(inter_vol, union_vol)  # 交集/并集，即IOU
        return jaccard  # (m, m, k)
    def condition(i,feat_labels, feat_scores,
             feat_ymin, feat_xmin, feat_ymax, feat_xmax):
        r = tf.less(i, tf.shape(labels))
        return r[0]
    # 該部分用于尋找實(shí)際中的框?qū)?yīng)特征層的哪個(gè)框
    def body(i, feat_labels, feat_scores,
             feat_ymin, feat_xmin, feat_ymax, feat_xmax):
        """
          更新功能標(biāo)簽、分?jǐn)?shù)和bbox。
            -JacCard>0.5時(shí)賦值；
        """
        # 取出第i個(gè)標(biāo)簽和第i個(gè)bboxes
        label = labels[i]  # 當(dāng)前圖片上第i個(gè)對(duì)象的標(biāo)簽
        bbox = bboxes[i]  # 當(dāng)前圖片上第i個(gè)對(duì)象的真實(shí)框bbox
        # 計(jì)算該box和所有anchor_box的IOU
        jaccard = jaccard_with_anchors(bbox)  # 當(dāng)前對(duì)象的bbox和當(dāng)前層的搜索網(wǎng)格IOU
        # 所有高于歷史的分的box被篩選
        mask = tf.greater(jaccard, feat_scores)  # 掩碼矩陣，IOU大于歷史得分的為T(mén)rue
        mask = tf.logical_and(mask, feat_scores > -0.5)
        imask = tf.cast(mask, tf.int64) #[1,0,1,1,0]
        fmask = tf.cast(mask, dtype)    #[1.,0.,1.,0. ... ]
        # Update values using mask.
        # 保證feat_labels存儲(chǔ)對(duì)應(yīng)位置得分最大對(duì)象標(biāo)簽，feat_scores存儲(chǔ)那個(gè)得分
        # (m, m, k) × 當(dāng)前類(lèi)別 + (1 - (m, m, k)) × (m, m, k)
        # 更新label記錄，此時(shí)的imask已經(jīng)保證了True位置當(dāng)前對(duì)像得分高于之前的對(duì)象得分，其他位置值不變
        # 將所有被認(rèn)為是label的框的值賦予feat_labels
        feat_labels = imask * label + (1 - imask) * feat_labels
        # 用于尋找最匹配的框
        feat_scores = tf.where(mask, jaccard, feat_scores)
        # 下面四個(gè)矩陣存儲(chǔ)對(duì)應(yīng)label的真實(shí)框坐標(biāo)
        # (m, m, k) × 當(dāng)前框坐標(biāo)scalar + (1 - (m, m, k)) × (m, m, k)
        feat_ymin = fmask * bbox[0] + (1 - fmask) * feat_ymin
        feat_xmin = fmask * bbox[1] + (1 - fmask) * feat_xmin
        feat_ymax = fmask * bbox[2] + (1 - fmask) * feat_ymax
        feat_xmax = fmask * bbox[3] + (1 - fmask) * feat_xmax
        return [i + 1, feat_labels, feat_scores,
                feat_ymin, feat_xmin, feat_ymax, feat_xmax]
    i = 0
    # 2、對(duì)所有的實(shí)際框都尋找其在特征層中對(duì)應(yīng)的點(diǎn)與其對(duì)應(yīng)的框，并將其標(biāo)簽找到。
    (i,feat_labels, feat_scores,feat_ymin, feat_xmin,
     feat_ymax, feat_xmax) = tf.while_loop(condition, body,
                                           [i,
                                            feat_labels, feat_scores,
                                            feat_ymin, feat_xmin,
                                            feat_ymax, feat_xmax])

    # Transform to center / size.
    # 3、轉(zhuǎn)化成ssd中網(wǎng)絡(luò)的輸出格式。
    feat_cy = (feat_ymax + feat_ymin) / 2.
    feat_cx = (feat_xmax + feat_xmin) / 2.
    feat_h = feat_ymax - feat_ymin
    feat_w = feat_xmax - feat_xmin
    # Encode features.
    # 利用公式進(jìn)行計(jì)算
    # 以搜索網(wǎng)格中心點(diǎn)為參考，真實(shí)框中心的偏移，單位長(zhǎng)度為網(wǎng)格hw
    feat_cy = (feat_cy - yref) / href / prior_scaling[0]
    feat_cx = (feat_cx - xref) / wref / prior_scaling[1]
    # log((m, m, k) / (m, m, 1)) * 5
    # 真實(shí)框?qū)捀?搜索網(wǎng)格寬高，取對(duì)
    feat_h = tf.log(feat_h / href) / prior_scaling[2]
    feat_w = tf.log(feat_w / wref) / prior_scaling[3]
    # Use SSD ordering: x / y / w / h instead of ours.(m, m, k, 4)
    feat_localizations = tf.stack([feat_cx, feat_cy, feat_w, feat_h], axis=-1)
    return feat_labels, feat_localizations, feat_scores

# 設(shè)置SSD網(wǎng)絡(luò)的參數(shù)
arg_scope = ssd_net.arg_scope(weight_decay=FLAGS.weight_decay,
                                data_format=DATA_FORMAT)
# 將圖片經(jīng)過(guò)網(wǎng)絡(luò)獲得它們的框的位置和prediction
with slim.arg_scope(arg_scope):
    _, localisations, logits, _ = \
        ssd_net.net(b_image, is_training=True)

# 計(jì)算loss值
n_positives_loss,n_negative_loss,localization_loss = ssd_net.losses(logits, localisations,
                                                        b_gclasses, b_glocalisations, b_gscores,
                                                        match_threshold=FLAGS.match_threshold,
                                                        negative_ratio=FLAGS.negative_ratio,
                                                        alpha=FLAGS.loss_alpha,
                                                        label_smoothing=FLAGS.label_smoothing)
# 會(huì)得到三個(gè)loss值，分別對(duì)應(yīng)正樣本，負(fù)樣本，定位
loss_all = n_positives_loss + n_negative_loss + localization_loss  

def ssd_losses(logits, localisations,
               gclasses, glocalisations, gscores,
               match_threshold=0.5,
               negative_ratio=3.,
               alpha=1.,
               label_smoothing=0.,
               device='/cpu:0',
               scope=None):
    with tf.name_scope(scope, 'ssd_losses'):
        lshape = tfe.get_shape(logits[0], 5)
        num_classes = lshape[-1]
        batch_size = lshape[0]
        # 鋪平所有vector
        flogits = []
        fgclasses = []
        fgscores = []
        flocalisations = []
        fglocalisations = []
        for i in range(len(logits)): # 按照?qǐng)D片循環(huán)
            flogits.append(tf.reshape(logits[i], [-1, num_classes]))
            fgclasses.append(tf.reshape(gclasses[i], [-1]))
            fgscores.append(tf.reshape(gscores[i], [-1]))
            flocalisations.append(tf.reshape(localisations[i], [-1, 4]))
            fglocalisations.append(tf.reshape(glocalisations[i], [-1, 4]))
        # 上一步所得的還存在batch個(gè)行里面，對(duì)應(yīng)batch個(gè)圖片
        # 這一步將batch個(gè)圖片平鋪到同一表上
        logits = tf.concat(flogits, axis=0)
        gclasses = tf.concat(fgclasses, axis=0)
        gscores = tf.concat(fgscores, axis=0)
        localisations = tf.concat(flocalisations, axis=0)
        glocalisations = tf.concat(fglocalisations, axis=0)
        dtype = logits.dtype
        # gscores中滿足正樣本得分的mask
        pmask = gscores > match_threshold
        fpmask = tf.cast(pmask, dtype)
        no_classes = tf.cast(pmask, tf.int32)
        nmask = tf.logical_and(tf.logical_not(pmask),# IOU達(dá)不到閾值的類(lèi)別搜索框位置記1
                               gscores > -0.5)
        fnmask = tf.cast(nmask, dtype)
        n_positives = tf.reduce_sum(fpmask)
        # 將預(yù)測(cè)結(jié)果轉(zhuǎn)化成比率
        predictions = slim.softmax(logits)
        nvalues = tf.where(nmask,
                           predictions[:, 0],   # 框內(nèi)無(wú)物體標(biāo)記為背景預(yù)測(cè)概率
                           1. - fnmask)         # 框內(nèi)有物體位置標(biāo)記為1
        nvalues_flat = tf.reshape(nvalues, [-1])
        # max_neg_entries為實(shí)際上負(fù)樣本的個(gè)數(shù)
        max_neg_entries = tf.cast(tf.reduce_sum(fnmask), tf.int32)
        # n_neg為正樣本的個(gè)數(shù)*3 + batch_size , 之所以+batchsize是因?yàn)槊總€(gè)圖最少有一個(gè)負(fù)樣本背景
        n_neg = tf.cast(negative_ratio * n_positives, tf.int32) + batch_size
        n_neg = tf.minimum(n_neg, max_neg_entries)
        # 找到n_neg個(gè)最不可能為背景的點(diǎn)
        val, idxes = tf.nn.top_k(-nvalues_flat, k=n_neg)
        max_hard_pred = -val[-1]
        # 在nmask找到n_neg個(gè)最不可能為背景的點(diǎn)（實(shí)際上它是背景，這樣二者的差就很大）
        nmask = tf.logical_and(nmask, nvalues < max_hard_pred)
        fnmask = tf.cast(nmask, dtype)
        n_negative = tf.reduce_sum(fnmask)
        # 交叉熵
        with tf.name_scope('cross_entropy_pos'):
            loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
                                                                  labels=gclasses)
            n_positives_loss = tf.div(tf.reduce_sum(loss * fpmask), n_positives + 0.1, name='value')
        with tf.name_scope('cross_entropy_neg'):
            loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
                                                                  labels=no_classes)
            n_negative_loss = tf.div(tf.reduce_sum(loss * fnmask), n_negative + 0.1, name='value')
        # Add localization loss: smooth L1, L2, ...
        with tf.name_scope('localization'):
            # Weights Tensor: positive mask + random negative.
            weights = tf.expand_dims(alpha * fpmask, axis=-1)
            loss = custom_layers.abs_smooth(localisations - glocalisations)
            localization_loss = tf.div(tf.reduce_sum(loss * weights), n_positives + 0.1, name='value')
        return n_positives_loss,n_negative_loss,localization_loss

################################優(yōu)化器設(shè)置##############################                                      
learning_rate = tf_utils.configure_learning_rate(FLAGS,
                                                        dataset.num_samples,
                                                        global_step)
optimizer = tf_utils.configure_optimizer(FLAGS, learning_rate)
train_op = slim.learning.create_train_op(loss_all, optimizer,
                                        summarize_gradients=True)
#################################訓(xùn)練并保存模型###########################
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)
config = tf.ConfigProto(log_device_placement=False,
                        gpu_options=gpu_options)
saver = tf.train.Saver(max_to_keep=5,
                        keep_checkpoint_every_n_hours=1.0,
                        write_version=2,
                        pad_step_number=False)
slim.learning.train(
    train_op,			# 優(yōu)化器
    logdir=FLAGS.train_dir,		# 保存模型的地址
    master='',
    is_chief=True,
    init_fn=tf_utils.get_init_fn(FLAGS),	# 微調(diào)已存在模型時(shí)，初始化參數(shù)
    number_of_steps=FLAGS.max_number_of_steps,		# 最大步數(shù)
    log_every_n_steps=FLAGS.log_every_n_steps,		# 多少時(shí)間進(jìn)行一次命令行輸出
    save_summaries_secs=FLAGS.save_summaries_secs,	# 進(jìn)行一次summary
    saver=saver,
    save_interval_secs=FLAGS.save_interval_secs,	# 多長(zhǎng)時(shí)間保存一次模型
    session_config=config,
    sync_optimizer=None)

DATASET_DIR=./tfrecords
TRAIN_DIR=./logs/
CHECKPOINT_PATH=./checkpoints/ssd_300_vgg.ckpt
python train_demo.py \
    --train_dir=${TRAIN_DIR} \
    --dataset_dir=${DATASET_DIR} \
    --dataset_name=pascalvoc_2012 \
    --dataset_split_name=train \
    --model_name=ssd_300_vgg \
    --checkpoint_path=${CHECKPOINT_PATH} \
    --save_summaries_secs=60 \
    --save_interval_secs=600 \
    --weight_decay=0.0005 \
    --optimizer=adam \
    --learning_rate=0.001 \
    --batch_size=8