Python tensorflow.boolean_mask函数代码示例

    def build_detector(self):
        img_size = self.config['image_size']
        self.image_ph = tf.placeholder(shape=[None, None, 3],
                                       dtype=tf.float32, name='img_ph')
        self.seg_ph = tf.placeholder(shape=[None, None], dtype=tf.int32, name='seg_ph')

        img = tf.image.resize_bilinear(tf.expand_dims(self.image_ph, 0),
                                       (img_size, img_size))
        self.net.create_trunk(img)

        if args.detect:
            self.net.create_multibox_head(self.loader.num_classes)
            confidence = tf.nn.softmax(tf.squeeze(self.net.outputs['confidence']))
            location = tf.squeeze(self.net.outputs['location'])
            self.nms(location, confidence, self.bboxer.tiling)

        if args.segment:
            self.net.create_segmentation_head(self.loader.num_classes)
            self.segmentation = self.net.outputs['segmentation']
            seg_shape = tf.shape(self.image_ph)[:2]
            self.segmentation = tf.image.resize_bilinear(self.segmentation, seg_shape)

            self.segmentation = tf.cast(tf.argmax(tf.squeeze(self.segmentation), axis=-1), tf.int32)
            self.segmentation = tf.reshape(self.segmentation, seg_shape)
            self.segmentation.set_shape([None, None])

            if not self.no_gt:
                easy_mask = self.seg_ph <= self.loader.num_classes
                predictions = tf.boolean_mask(self.segmentation, easy_mask)
                labels = tf.boolean_mask(self.seg_ph, easy_mask)
                self.mean_iou, self.iou_update = mean_iou(predictions, labels, self.loader.num_classes)
            else:
                self.mean_iou = tf.constant(0)
                self.iou_update = tf.constant(0)

    def remap_keys(sparse_tensor):
        # Current indices of our SparseTensor that we need to fix
        bad_indices = sparse_tensor.indices # shape = (current_batch_size * (number_of_items/users[i] + 1), 2)
        # Current values of our SparseTensor that we need to fix
        bad_values = sparse_tensor.values # shape = (current_batch_size * (number_of_items/users[i] + 1),)

        # Since batch is ordered, the last value for a batch index is the user
        # Find where the batch index chages to extract the user rows
        # 1 where user, else 0
        user_mask = tf.concat(values = [bad_indices[1:,0] - bad_indices[:-1,0], tf.constant(value = [1], dtype = tf.int64)], axis = 0) # shape = (current_batch_size * (number_of_items/users[i] + 1), 2)

        # Mask out the user rows from the values
        good_values = tf.boolean_mask(tensor = bad_values, mask = tf.equal(x = user_mask, y = 0)) # shape = (current_batch_size * number_of_items/users[i],)
        item_indices = tf.boolean_mask(tensor = bad_indices, mask = tf.equal(x = user_mask, y = 0)) # shape = (current_batch_size * number_of_items/users[i],)
        user_indices = tf.boolean_mask(tensor = bad_indices, mask = tf.equal(x = user_mask, y = 1))[:, 1] # shape = (current_batch_size,)

        good_user_indices = tf.gather(params = user_indices, indices = item_indices[:,0]) # shape = (current_batch_size * number_of_items/users[i],)

        # User and item indices are rank 1, need to make rank 1 to concat
        good_user_indices_expanded = tf.expand_dims(input = good_user_indices, axis = -1) # shape = (current_batch_size * number_of_items/users[i], 1)
        good_item_indices_expanded = tf.expand_dims(input = item_indices[:, 1], axis = -1) # shape = (current_batch_size * number_of_items/users[i], 1)
        good_indices = tf.concat(values = [good_user_indices_expanded, good_item_indices_expanded], axis = 1) # shape = (current_batch_size * number_of_items/users[i], 2)

        remapped_sparse_tensor = tf.SparseTensor(indices = good_indices, values = good_values, dense_shape = sparse_tensor.dense_shape)
        return remapped_sparse_tensor

    def __init__(self,
                 prev_actions_logp,
                 actions_logp,
                 action_kl,
                 actions_entropy,
                 values,
                 valid_mask,
                 advantages,
                 value_targets,
                 vf_loss_coeff=0.5,
                 entropy_coeff=-0.01,
                 clip_param=0.3):

        logp_ratio = tf.exp(actions_logp - prev_actions_logp)

        surrogate_loss = tf.minimum(
            advantages * logp_ratio,
            advantages * tf.clip_by_value(logp_ratio, 1 - clip_param,
                                          1 + clip_param))

        self.mean_kl = tf.reduce_mean(action_kl)
        self.pi_loss = -tf.reduce_sum(surrogate_loss)

        # The baseline loss
        delta = tf.boolean_mask(values - value_targets, valid_mask)
        self.value_targets = value_targets
        self.vf_loss = 0.5 * tf.reduce_sum(tf.square(delta))

        # The entropy loss
        self.entropy = tf.reduce_sum(
            tf.boolean_mask(actions_entropy, valid_mask))

        # The summed weighted loss
        self.total_loss = (self.pi_loss + self.vf_loss * vf_loss_coeff +
                           self.entropy * entropy_coeff)

def yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = .6):
    """Filters YOLO boxes by thresholding on object and class confidence.
    
    Arguments:
    box_confidence -- tensor of shape (19, 19, 5, 1)
    boxes -- tensor of shape (19, 19, 5, 4)
    box_class_probs -- tensor of shape (19, 19, 5, 80)
    threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box
    
    Returns:
    scores -- tensor of shape (None,), containing the class probability score for selected boxes
    boxes -- tensor of shape (None, 4), containing (b_x, b_y, b_h, b_w) coordinates of selected boxes
    classes -- tensor of shape (None,), containing the index of the class detected by the selected boxes
    
    Note: "None" is here because you don't know the exact number of selected boxes, as it depends on the threshold. 
    For example, the actual output size of scores would be (10,) if there are 10 boxes.
    """
    
    # Step 1: Compute box scores
    box_scores = box_confidence * box_class_probs  # [19, 19, 5, 1] * [19, 19, 5, 80] = [19, 19, 5, 80]
    
    # Step 2: Find the box_classes thanks to the max box_scores, keep track of the corresponding score
    box_classes      = K.argmax(box_scores, axis=-1)
    box_class_scores = K.max(box_scores, axis = -1, keepdims = False)
        
    # Step 3: Create a filtering mask based on "box_class_scores" by using "threshold". The mask should have the
    # same dimension as box_class_scores, and be True for the boxes you want to keep (with probability >= threshold)
    filtering_mask = box_class_scores >= threshold
        
    # Step 4: Apply the mask to scores, boxes and classes
    scores  = tf.boolean_mask(box_class_scores, filtering_mask) 
    boxes   = tf.boolean_mask(boxes, filtering_mask) 
    classes = tf.boolean_mask(box_classes, filtering_mask) 
        
    return scores, boxes, classes

    def nms(self, localization, confidence, tiling):
        good_bboxes = decode_bboxes(localization, tiling)

        not_crap_mask = tf.reduce_max(confidence[:, 1:], axis=-1) >= args.conf_thresh
        good_bboxes = tf.boolean_mask(good_bboxes, not_crap_mask)
        confidence = tf.boolean_mask(confidence, not_crap_mask)

        self.detection_list = []
        self.score_list = []
        for i in range(1, self.loader.num_classes):
            class_mask = tf.greater(confidence[:, i], args.conf_thresh)
            class_scores = tf.boolean_mask(confidence[:, i], class_mask)
            class_bboxes = tf.boolean_mask(good_bboxes, class_mask)

            K = tf.minimum(tf.size(class_scores), args.top_k_nms)
            _, top_k_inds = tf.nn.top_k(class_scores, K)
            top_class_scores = tf.gather(class_scores, top_k_inds)
            top_class_bboxes = tf.gather(class_bboxes, top_k_inds)

            final_inds = tf.image.non_max_suppression(top_class_bboxes,
                                                        top_class_scores,
                                                        max_output_size=args.top_k_after_nms,
                                                        iou_threshold=args.nms_thresh)

            final_class_bboxes = tf.gather(top_class_bboxes, final_inds)
            final_scores = tf.gather(top_class_scores, final_inds)
            self.detection_list.append(final_class_bboxes)
            self.score_list.append(final_scores)

def yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = .6):
    """Filters YOLO boxes by thresholding on object and class confidence.

    Arguments:
    box_confidence -- tensor of shape (19, 19, 5, 1)
    boxes -- tensor of shape (19, 19, 5, 4)
    box_class_probs -- tensor of shape (19, 19, 5, 80)
    threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box

    Returns:
    scores -- tensor of shape (None,), containing the class probability score for selected boxes
    boxes -- tensor of shape (None, 4), containing (b_x, b_y, b_h, b_w) coordinates of selected boxes
    classes -- tensor of shape (None,), containing the index of the class detected by the selected boxes

    """

    # Step 1: Compute box scores
    box_scores = box_confidence*box_class_probs

    # Step 2: Find the box_classes thanks to the max box_scores, keep track of the corresponding score
    box_classes = K.argmax(box_scores, axis=-1)
    box_class_scores = K.max(box_scores, axis=-1)

    # Step 3: Create a filtering mask based on "box_class_scores" by using "threshold". The mask should have the
    # same dimension as box_class_scores, and be True for the boxes you want to keep (with probability >= threshold)
    filtering_mask = box_class_scores >= threshold

    # Step 4: Apply the mask to scores, boxes and classes
    scores = tf.boolean_mask(box_class_scores,filtering_mask)
    boxes = tf.boolean_mask(boxes,filtering_mask)
    classes = tf.boolean_mask(box_classes,filtering_mask)

    return scores, boxes, classes

def roc_auc_score(y_pred, y_true):
    """ ROC AUC Score.

    Approximates the Area Under Curve score, using approximation based on
    the Wilcoxon-Mann-Whitney U statistic.

    Yan, L., Dodier, R., Mozer, M. C., & Wolniewicz, R. (2003).
    Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic.

    Measures overall performance for a full range of threshold levels.

    Arguments:
        y_pred: `Tensor`. Predicted values.
        y_true: `Tensor` . Targets (labels), a probability distribution.

    """
    with tf.name_scope("RocAucScore"):

        pos = tf.boolean_mask(y_pred, tf.cast(y_true, tf.bool))
        neg = tf.boolean_mask(y_pred, ~tf.cast(y_true, tf.bool))

        pos = tf.expand_dims(pos, 0)
        neg = tf.expand_dims(neg, 1)

        # original paper suggests performance is robust to exact parameter choice
        gamma = 0.2
        p     = 3

        difference = tf.zeros_like(pos * neg) + pos - neg - gamma

        masked = tf.boolean_mask(difference, difference < 0.0)

        return tf.reduce_sum(tf.pow(-masked, p))

    def add_loss_op(self, preds):
        """Adds Ops for the loss function to the computational graph.

        TODO: Compute averaged cross entropy loss for the predictions.
        Importantly, you must ignore the loss for any masked tokens.

        Hint: You might find tf.boolean_mask useful to mask the losses on masked tokens.
        Hint: You can use tf.nn.sparse_softmax_cross_entropy_with_logits to simplify your
                    implementation. You might find tf.reduce_mean useful.
        Args:
            pred: A tensor of shape (batch_size, max_length, n_classes) containing the output of the neural
                  network before the softmax layer.
        Returns:
            loss: A 0-d tensor (scalar)
        """
        ### YOUR CODE HERE (~2-4 lines)
        logits=tf.boolean_mask(preds,self.mask_placeholder)
        labels=tf.boolean_mask(self.labels_placeholder,self.mask_placeholder)
        loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
                logits=logits,
                labels=labels)

        loss = tf.reduce_mean(loss)
        ### END YOUR CODE
        return loss

def boolean_mask(boxlist, indicator, fields=None, scope=None,
                 use_static_shapes=False, indicator_sum=None):
  """Select boxes from BoxList according to indicator and return new BoxList.

  `boolean_mask` returns the subset of boxes that are marked as "True" by the
  indicator tensor. By default, `boolean_mask` returns boxes corresponding to
  the input index list, as well as all additional fields stored in the boxlist
  (indexing into the first dimension).  However one can optionally only draw
  from a subset of fields.

  Args:
    boxlist: BoxList holding N boxes
    indicator: a rank-1 boolean tensor
    fields: (optional) list of fields to also gather from.  If None (default),
      all fields are gathered from.  Pass an empty fields list to only gather
      the box coordinates.
    scope: name scope.
    use_static_shapes: Whether to use an implementation with static shape
      gurantees.
    indicator_sum: An integer containing the sum of `indicator` vector. Only
      required if `use_static_shape` is True.

  Returns:
    subboxlist: a BoxList corresponding to the subset of the input BoxList
      specified by indicator
  Raises:
    ValueError: if `indicator` is not a rank-1 boolean tensor.
  """
  with tf.name_scope(scope, 'BooleanMask'):
    if indicator.shape.ndims != 1:
      raise ValueError('indicator should have rank 1')
    if indicator.dtype != tf.bool:
      raise ValueError('indicator should be a boolean tensor')
    if use_static_shapes:
      if not (indicator_sum and isinstance(indicator_sum, int)):
        raise ValueError('`indicator_sum` must be a of type int')
      selected_positions = tf.to_float(indicator)
      indexed_positions = tf.cast(
          tf.multiply(
              tf.cumsum(selected_positions), selected_positions),
          dtype=tf.int32)
      one_hot_selector = tf.one_hot(
          indexed_positions - 1, indicator_sum, dtype=tf.float32)
      sampled_indices = tf.cast(
          tf.tensordot(
              tf.to_float(tf.range(tf.shape(indicator)[0])),
              one_hot_selector,
              axes=[0, 0]),
          dtype=tf.int32)
      return gather(boxlist, sampled_indices, use_static_shapes=True)
    else:
      subboxlist = box_list.BoxList(tf.boolean_mask(boxlist.get(), indicator))
      if fields is None:
        fields = boxlist.get_extra_fields()
      for field in fields:
        if not boxlist.has_field(field):
          raise ValueError('boxlist must contain all specified fields')
        subfieldlist = tf.boolean_mask(boxlist.get_field(field), indicator)
        subboxlist.add_field(field, subfieldlist)
      return subboxlist

    def loss(self, logits, labels, regularization):
        """Adds to the inference model the layers required to generate loss."""
        with tf.name_scope('loss'):
            with tf.name_scope('var_loss'):
                labels = tf.cast(labels, tf.float32)
                shape = labels.get_shape()

                same_class = tf.boolean_mask(logits, tf.equal(labels, tf.ones(shape)))
                diff_class = tf.boolean_mask(logits, tf.not_equal(labels, tf.ones(shape)))
                same_mean, same_var = tf.nn.moments(same_class, [0])
                diff_mean, diff_var = tf.nn.moments(diff_class, [0])
                var_loss = same_var + diff_var

            with tf.name_scope('mean_loss'):
                mean_loss = self.lamda * tf.where(tf.greater(self.mu - (same_mean - diff_mean), 0),
                                                  self.mu - (same_mean - diff_mean), 0)

            with tf.name_scope('regularization'):
                regularization *= tf.add_n(self.regularizers)

            loss = var_loss + mean_loss + regularization

            # Summaries for TensorBoard.
            tf.summary.scalar('loss/total', loss)
            with tf.name_scope('averages'):
                averages = tf.train.ExponentialMovingAverage(0.9)
                op_averages = averages.apply([var_loss, mean_loss, regularization, loss])
                tf.summary.scalar('loss/avg/var_loss', averages.average(var_loss))
                tf.summary.scalar('loss/avg/mean_loss', averages.average(mean_loss))
                tf.summary.scalar('loss/avg/regularization', averages.average(regularization))
                tf.summary.scalar('loss/avg/total', averages.average(loss))
                with tf.control_dependencies([op_averages]):
                    loss_average = tf.identity(averages.average(loss), name='control')
            return loss, loss_average

 def shortlist_insert():
   larger_ids = tf.boolean_mask(tf.to_int64(ids), larger_scores)
   larger_score_values = tf.boolean_mask(scores, larger_scores)
   shortlist_ids, new_ids, new_scores = self.ops.top_n_insert(
       self.sl_ids, self.sl_scores, larger_ids, larger_score_values)
   u1 = tf.scatter_update(self.sl_ids, shortlist_ids, new_ids)
   u2 = tf.scatter_update(self.sl_scores, shortlist_ids, new_scores)
   return tf.group(u1, u2)

def lamb_func(logit, logic, lamb):
    logit_pos = tf.boolean_mask(logit, logic)
    logit_neg = tf.boolean_mask(logit, tf.logical_not(logic))
    logit_neg_exp = tf.exp(logit_neg * lamb)
    z = tf.reduce_mean(logit_neg_exp)
    left = tf.truediv(tf.reduce_mean(logit_neg * logit_neg_exp), z)
    right = tf.reduce_mean(logit_pos)
    return left, right

def rpn_losses(anchor_labels, anchor_boxes, label_logits, box_logits):
    """
    Args:
        anchor_labels: fHxfWxNA
        anchor_boxes: fHxfWxNAx4, encoded
        label_logits:  fHxfWxNA
        box_logits: fHxfWxNAx4

    Returns:
        label_loss, box_loss
    """
    with tf.device('/cpu:0'):
        valid_mask = tf.stop_gradient(tf.not_equal(anchor_labels, -1))
        pos_mask = tf.stop_gradient(tf.equal(anchor_labels, 1))
        nr_valid = tf.stop_gradient(tf.count_nonzero(valid_mask, dtype=tf.int32), name='num_valid_anchor')
        nr_pos = tf.count_nonzero(pos_mask, dtype=tf.int32, name='num_pos_anchor')

        valid_anchor_labels = tf.boolean_mask(anchor_labels, valid_mask)
    valid_label_logits = tf.boolean_mask(label_logits, valid_mask)

    with tf.name_scope('label_metrics'):
        valid_label_prob = tf.nn.sigmoid(valid_label_logits)
        summaries = []
        with tf.device('/cpu:0'):
            for th in [0.5, 0.2, 0.1]:
                valid_prediction = tf.cast(valid_label_prob > th, tf.int32)
                nr_pos_prediction = tf.reduce_sum(valid_prediction, name='num_pos_prediction')
                pos_prediction_corr = tf.count_nonzero(
                    tf.logical_and(
                        valid_label_prob > th,
                        tf.equal(valid_prediction, valid_anchor_labels)),
                    dtype=tf.int32)
                summaries.append(tf.truediv(
                    pos_prediction_corr,
                    nr_pos, name='recall_th{}'.format(th)))
                precision = tf.to_float(tf.truediv(pos_prediction_corr, nr_pos_prediction))
                precision = tf.where(tf.equal(nr_pos_prediction, 0), 0.0, precision, name='precision_th{}'.format(th))
                summaries.append(precision)
        add_moving_summary(*summaries)

    label_loss = tf.nn.sigmoid_cross_entropy_with_logits(
        labels=tf.to_float(valid_anchor_labels), logits=valid_label_logits)
    label_loss = tf.reduce_mean(label_loss, name='label_loss')

    pos_anchor_boxes = tf.boolean_mask(anchor_boxes, pos_mask)
    pos_box_logits = tf.boolean_mask(box_logits, pos_mask)
    delta = 1.0 / 9
    box_loss = tf.losses.huber_loss(
        pos_anchor_boxes, pos_box_logits, delta=delta,
        reduction=tf.losses.Reduction.SUM) / delta
    box_loss = tf.div(
        box_loss,
        tf.cast(nr_valid, tf.float32), name='box_loss')

    add_moving_summary(label_loss, box_loss, nr_valid, nr_pos)
    return label_loss, box_loss

  def remap_keys(sparse_tensor):
    # Current indices of our SparseTensor that we need to fix
    bad_indices = sparse_tensor.indices
    # Current values of our SparseTensor that we need to fix
    bad_values = sparse_tensor.values 
  
    # Group by the batch_indices and get the count for each  
    size = tf.segment_sum(data = tf.ones_like(bad_indices[:,0], dtype = tf.int64), segment_ids = bad_indices[:,0]) - 1
    # The number of batch_indices (this should be batch_size unless it is a partially full batch)
    length = tf.shape(size, out_type = tf.int64)[0]
    # Finds the cumulative sum which we can use for indexing later
    cum = tf.cumsum(size)
    # The offsets between each example in the batch due to our concatentation of the keys in the decode_example method
    length_range = tf.range(start = 0, limit = length, delta = 1, dtype = tf.int64)
    # Indices of the SparseTensor's indices member of the rows we added by the concatentation of our keys in the decode_example method
    cum_range = cum + length_range

    # The keys that we have extracted back out of our concatentated SparseTensor
    gathered_indices = tf.squeeze(tf.gather(bad_indices, cum_range)[:,1])

    # The enumerated row indices of the SparseTensor's indices member
    sparse_indices_range = tf.range(tf.shape(bad_indices, out_type = tf.int64)[0], dtype = tf.int64)

    # We want to find here the row indices of the SparseTensor's indices member that are of our actual data and not the concatentated rows
    # So we want to find the intersection of the two sets and then take the opposite of that
    x = sparse_indices_range
    s = cum_range

    # Number of multiples we are going to tile x, which is our sparse_indices_range
    tile_multiples = tf.concat([tf.ones(tf.shape(tf.shape(x)), dtype=tf.int64), tf.shape(s, out_type = tf.int64)], axis = 0)
    # Expands x, our sparse_indices_range, into a rank 2 tensor and then multiplies the rows by 1 (no copying) and the columns by the number of examples in the batch
    x_tile = tf.tile(tf.expand_dims(x, -1), tile_multiples)
    # Essentially a vectorized logical or, that we then negate
    x_not_in_s = ~tf.reduce_any(tf.equal(x_tile, s), -1)

    # The SparseTensor's indices that are our actual data by using the boolean_mask we just made above applied to the entire indices member of our SparseTensor
    selected_indices = tf.boolean_mask(tensor = bad_indices, mask = x_not_in_s, axis = 0)
    # Apply the same boolean_mask to the entire values member of our SparseTensor to get the actual values data
    selected_values = tf.boolean_mask(tensor = bad_values, mask = x_not_in_s, axis = 0)

    # Need to replace the first column of our selected_indices with keys, so we first need to tile our gathered_indices
    tiling = tf.tile(input = tf.expand_dims(gathered_indices[0], -1), multiples = tf.expand_dims(size[0] , -1))
    
    # We have to repeatedly apply the tiling to each example in the batch
    # Since it is jagged we cannot use tf.map_fn due to the stacking of the TensorArray, so we have to create our own custom version
    def loop_body(i, tensor_grow):
      return i + 1, tf.concat(values = [tensor_grow, tf.tile(input = tf.expand_dims(gathered_indices[i], -1), multiples = tf.expand_dims(size[i] , -1))], axis = 0)

    _, result = tf.while_loop(lambda i, tensor_grow: i < length, loop_body, [tf.constant(1, dtype = tf.int64), tiling])
    
    # Concatenate tiled keys with the 2nd column of selected_indices
    selected_indices_fixed = tf.concat([tf.expand_dims(result, -1), tf.expand_dims(selected_indices[:, 1], -1)], axis = 1)
    
    # Combine everything together back into a SparseTensor
    remapped_sparse_tensor = tf.SparseTensor(indices = selected_indices_fixed, values = selected_values, dense_shape = sparse_tensor.dense_shape)
    return remapped_sparse_tensor

    def _build_detector(self):  # 解析网络的预测结果, 这里采用了判断预测框类别, 再 NMS 的预测策略
        """Interpret the net output and get the predicted boxes"""
        # the width and height of orignal image
        self.width = tf.placeholder(tf.float32, name="img_w")
        self.height = tf.placeholder(tf.float32, name="img_h")
        # get class prob, confidence, boxes from net output
        idx1 = self.S * self.S * self.C
        idx2 = idx1 + self.S * self.S * self.B
        # class prediction; 具体的位置都是自己设置的, 因为输出是一维的, 所以直接切出来合适的大小, 通过反向传播来学习
        class_probs = tf.reshape(self.predicts[0, :idx1], [self.S, self.S, self.C])
        # confidence
        confs = tf.reshape(self.predicts[0, idx1:idx2], [self.S, self.S, self.B])
        # boxes -> (x, y, w, h)
        boxes = tf.reshape(self.predicts[0, idx2:], [self.S, self.S, self.B, 4])
        # 为什么是二维的呢, 输出不应该是一维的吗

        # convert the x, y to the coordinates relative to the top left point of the image
        # the predictions of w, h are the square root
        # multiply the width and height of image;
        # 这里是 decode 过程 (得到 box 的真实位置), 可以如下:
        # 就是把预测值加上 offset, 除以 self.S 将坐标转换为 [0, 1] 范围, 乘以 self.width 是转化为实际位置
        boxes = tf.stack([(boxes[:, :, :, 0] + tf.constant(self.x_offset, dtype=tf.float32)) / self.S * self.width,
                          (boxes[:, :, :, 1] + tf.constant(self.y_offset, dtype=tf.float32)) / self.S * self.height,
                          tf.square(boxes[:, :, :, 2]) * self.width,
                          tf.square(boxes[:, :, :, 3]) * self.height], axis=3)

        # class-specific confidence scores [S, S, B, C]
        scores = tf.expand_dims(confs, -1) * tf.expand_dims(class_probs, 2)  # 7x7x2x1 * 7x7x1x20 = 7x7x2x20; 好神奇

        scores = tf.reshape(scores, [-1, self.C])  # [S*S*B, C]
        boxes = tf.reshape(boxes, [-1, 4])  # [S*S*B, 4]; 这里用这种方式实现了论文里的思路

        # find each box class, only select the max score
        box_classes = tf.argmax(scores, axis=1)  # 求出每个 score 20 个分类中最大值的索引
        box_class_scores = tf.reduce_max(scores, axis=1)  # 找到对应维中的最大值
        # print(sess.run(tf.argmax([[1, 2], [3, 4]], axis=1)))  # [1 1]
        # print(sess.run(tf.reduce_max([[1, 2], [3, 4]], axis=1)))  # [2 4]

        # filter the boxes by the score threshold
        filter_mask = box_class_scores >= self.threshold
        scores = tf.boolean_mask(box_class_scores, filter_mask)
        boxes = tf.boolean_mask(boxes, filter_mask)
        box_classes = tf.boolean_mask(box_classes, filter_mask)

        # non max suppression (do not distinguish different classes)
        # ref: https://tensorflow.google.cn/api_docs/python/tf/image/non_max_suppression
        # box (x, y, w, h) -> box (x1, y1, x2, y2)
        _boxes = tf.stack([boxes[:, 0] - 0.5 * boxes[:, 2], boxes[:, 1] - 0.5 * boxes[:, 3],
                           boxes[:, 0] + 0.5 * boxes[:, 2], boxes[:, 1] + 0.5 * boxes[:, 3]], axis=1)
        nms_indices = tf.image.non_max_suppression(_boxes, scores,
                                                   self.max_output_size, self.iou_threshold)
        self.scores = tf.gather(scores, nms_indices)
        self.boxes = tf.gather(boxes, nms_indices)
        self.box_classes = tf.gather(box_classes, nms_indices)