keras-yolo3项目之三：model.py文件

model.py文件是用keras模块编写的yolo模型文件，主要包含14个函数：

• DarknetConv2D：为二维卷积层设置Darknet参数。
• DarknetConv2D_BN_Leaky：由二维卷积层DarknetConv2D与正则化层BatchNormalization、激活函数LeakyReLU组合而成。
• resblock_body：封装了一个循环体：包含2个卷积核大小不同的DarknetConv2D_BN_Leaky
• darknet_body：Darknent网络结构，含有52个二维卷积层。
• make_last_layers：最后几个函数层，由6个DarknetConv2D_BN_Leaky层和1个Conv2D_linear层组成。
• yolo_body：yolo网络结构。
• tiny_yolo_body：tiny_yolo网络结构。
• yolo_head：将最终网络层特征转换为边界框参数。
• yolo_correct_boxes：获取正确的边界框。
• yolo_boxes_and_scores：处理卷积层输出。
• yolo_eval：在给定的输入和返回过滤框上评估YOLO模型。
• preprocess_true_boxes：将true_boxes预处理为训练输入格式
• box_iou：检测框与原标注框的交并比。
• yolo_loss：模型损失。

详细内容如下：

"""YOLO_v3 Model Defined in Keras."""

from functools import wraps

import numpy as np
import tensorflow as tf
from keras import backend as K
from keras.layers import Conv2D, Add, ZeroPadding2D, UpSampling2D, Concatenate, MaxPooling2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.regularizers import l2

from yolo3.utils import compose


@wraps(Conv2D)
def DarknetConv2D(*args, **kwargs):
    """
    Wrapper to set Darknet parameters for Convolution2D.
    为2维卷积层设置Darknet参数
    
    返回带有Darknet参数的2维卷积层
    """
    darknet_conv_kwargs = {'kernel_regularizer': l2(5e-4)}
    darknet_conv_kwargs['padding'] = 'valid' if kwargs.get('strides')==(2,2) else 'same'
    darknet_conv_kwargs.update(kwargs)
    return Conv2D(*args, **darknet_conv_kwargs)    #带有Darknet参数的2维卷积层

def DarknetConv2D_BN_Leaky(*args, **kwargs):
    """
    Darknet Convolution2D followed by BatchNormalization and LeakyReLU.
    封装3个网络层：
    1.带有Darknet参数的2维卷积层
    2.批量正则化网络层：按批正则化前一个网络层的激活
    3.激活函数层
    """
    no_bias_kwargs = {'use_bias': False}
    no_bias_kwargs.update(kwargs)
    return compose(
        DarknetConv2D(*args, **no_bias_kwargs),    #带有Darknet参数的2维卷积层
        BatchNormalization(),    #按批正则化前一个网络层的激活
        LeakyReLU(alpha=0.1))    #激活函数

def resblock_body(x, num_filters, num_blocks):
    '''
    A series of resblocks starting with a downsampling Convolution2D
    封装了一个循环体：包含2个卷积核大小不同的DarknetConv2D_BN_Leaky
    x：输入的图像张量
    num_filters：输出空间的维度
    num_blocks：循环次数
    
    本函数包含2个步骤：
    1.填充,计算DarknetConv2D_BN_Leaky,输出维度为num_filters的x;
    2.循环累加(循环体),将步骤1的输出作为该步骤的初始输入x:
    首先计算1*1卷积核,然后将1*1卷积核的结果作为3*3卷积核的输入，计算出结果y
    循环累加的初始输入x与得到的y进行相加,将相加结果赋值给x,进入下一次循环
    '''
    # Darknet uses left and top padding instead of 'same' mode
    x = ZeroPadding2D(((1,0),(1,0)))(x)    #对2维输入的的左侧和上侧进行0填充,((1,0),(1,0))表示((上,下),(左,右))
    x = DarknetConv2D_BN_Leaky(num_filters, (3,3), strides=(2,2))(x)   #3*3的卷积核，卷积步长(2,2)
    
    for i in range(num_blocks):
        y = compose(
                DarknetConv2D_BN_Leaky(num_filters//2, (1,1)),    #1*1的卷积核
                DarknetConv2D_BN_Leaky(num_filters, (3,3)))(x)    #3*3的卷积核
        x = Add()([x,y])    #求和,输出维度与x,y一致
    return x

def darknet_body(x):
    '''
    Darknent body having 52 Convolution2D layers
    Darknent网络层主干，主要作用是对图像进行特征提取，包含52个2维卷积核层：52=1+3+5+17+17+9
    1个DarknetConv2D_BN_Leaky里包含1个2维卷积层,1个批量正则化层,1个激活层
    1个resblock_body包含1+2*num_blocks个DarknetConv2D_BN_Leaky
    '''
    x = DarknetConv2D_BN_Leaky(32, (3,3))(x)    #1
    x = resblock_body(x, 64, 1)    #1+2*1=3
    x = resblock_body(x, 128, 2)    #1+2*2=5
    x = resblock_body(x, 256, 8)    #1+2*8=17
    x = resblock_body(x, 512, 8)    #1+2*8=17
    x = resblock_body(x, 1024, 4)    #1+2*4=9
    return x

def make_last_layers(x, num_filters, out_filters):
    '''
    6 Conv2D_BN_Leaky layers followed by a Conv2D_linear layer
    7个2维卷积层
    5个DarknetConv2D_BN_Leaky计算得x
    1个DarknetConv2D_BN_Leaky,1个DarknetConv2D计算得y
    
    num_filters：中间卷积层x得输出通道数，也可以理解为特征数量
    out_filters：最终层y的通道数
    返回x,y
    '''
    x = compose(
            DarknetConv2D_BN_Leaky(num_filters, (1,1)),
            DarknetConv2D_BN_Leaky(num_filters*2, (3,3)),
            DarknetConv2D_BN_Leaky(num_filters, (1,1)),
            DarknetConv2D_BN_Leaky(num_filters*2, (3,3)),
            DarknetConv2D_BN_Leaky(num_filters, (1,1)))(x)
    y = compose(
            DarknetConv2D_BN_Leaky(num_filters*2, (3,3)),
            DarknetConv2D(out_filters, (1,1)))(x)
    return x, y


def yolo_body(inputs, num_anchors, num_classes):
    """
    Create YOLO_V3 model CNN body in Keras.
    YOLO_V3卷积神经网络主干，共包含75个卷积层
    
    1.Darknent模型，包含52个卷积层  
    2.Darknent模型的输出作为make_last_layers输入,输出x,y1，包含7个卷积层  
    3.将x进行DarknetConv2D_BN_Leaky,UpSampling2D计算，并将结果与Darknent模型的152层的输出进行拼接,得x，包含1个卷积层  
    4.将输出x作为make_last_layers输入,输出x,y2，包含7个卷积层  
    5.将x进行DarknetConv2D_BN_Leaky,UpSampling2D计算，并将结果与Darknent模型的92层的输出进行拼接,得x，包含1个卷积层  
    6.将输出x作为make_last_layers输入,输出x,y3，包含7个卷积层  
    """
    darknet = Model(inputs, darknet_body(inputs))   #Model(inputs, outputs, name=None)
    x, y1 = make_last_layers(darknet.output, 512, num_anchors*(num_classes+5))

    x = compose(
            DarknetConv2D_BN_Leaky(256, (1,1)),
            UpSampling2D(2))(x)
    x = Concatenate()([x,darknet.layers[152].output])    #拼接
    x, y2 = make_last_layers(x, 256, num_anchors*(num_classes+5))

    x = compose(
            DarknetConv2D_BN_Leaky(128, (1,1)),
            UpSampling2D(2))(x)
    x = Concatenate()([x,darknet.layers[92].output])
    x, y3 = make_last_layers(x, 128, num_anchors*(num_classes+5))

    return Model(inputs, [y1,y2,y3])

def tiny_yolo_body(inputs, num_anchors, num_classes):
    '''
    Create Tiny YOLO_v3 model CNN body in keras.
    轻量级YOLOV3卷积神经网络主干,包含12个卷积层
    
    1.5个DarknetConv2D_BN_Leaky计算得x1  
    2.将x1作为输入计算3个DarknetConv2D_BN_Leaky得x2  
    3.将x2作为输入计算DarknetConv2D_BN_Leaky,DarknetConv2D得y1  
    4.将x2作为输入计算DarknetConv2D_BN_Leaky,UpSampling2D,结果替换x2  
    5.将[x2,x1]进行Concatenate,DarknetConv2D_BN_Leaky,DarknetConv2D计算得y2  
    '''
    x1 = compose(
            DarknetConv2D_BN_Leaky(16, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),    #池化层
            DarknetConv2D_BN_Leaky(32, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(64, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(128, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(256, (3,3)))(inputs)
    x2 = compose(
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(512, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(1,1), padding='same'),
            DarknetConv2D_BN_Leaky(1024, (3,3)),
            DarknetConv2D_BN_Leaky(256, (1,1)))(x1)
    y1 = compose(
            DarknetConv2D_BN_Leaky(512, (3,3)),
            DarknetConv2D(num_anchors*(num_classes+5), (1,1)))(x2)

    x2 = compose(
            DarknetConv2D_BN_Leaky(128, (1,1)),
            UpSampling2D(2))(x2)
    y2 = compose(
            Concatenate(),
            DarknetConv2D_BN_Leaky(256, (3,3)),
            DarknetConv2D(num_anchors*(num_classes+5), (1,1)))([x2,x1])

    return Model(inputs, [y1,y2])


def yolo_head(feats, anchors, num_classes, input_shape, calc_loss=False):
    """Convert final layer features to bounding box parameters.
    将最终网络层特征转换为边界框参数。
    
    feats：yolov3网络的输出特征y1 or y2 or y3  
    anchors：聚类所得标注框,默认9个  
    num_classes：类别数量  
    input_shape：输入形状  
    返回检测框的中心坐标xy，宽高wh，置信度,类别概率
    """
    num_anchors = len(anchors)
    # Reshape to batch, height, width, num_anchors, box_params.
    anchors_tensor = K.reshape(K.constant(anchors), [1, 1, 1, num_anchors, 2])

    grid_shape = K.shape(feats)[1:3] # height, width,feats的第2，3个元素,即(width,feats)
    grid_y = K.tile(K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]),
        [1, grid_shape[1], 1, 1])    #元素复制扩张(width,feats,1,1)
    grid_x = K.tile(K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]),
        [grid_shape[0], 1, 1, 1])
    grid = K.concatenate([grid_x, grid_y])    #拼接
    grid = K.cast(grid, K.dtype(feats))    #转换元素类型

    feats = K.reshape(
        feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])

    # Adjust preditions to each spatial grid point and anchor size.
    # 调整检测框大小
    box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(grid_shape[::-1], K.dtype(feats))    #检测框中心坐标
    box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(input_shape[::-1], K.dtype(feats))    #检测框宽高
    box_confidence = K.sigmoid(feats[..., 4:5])    #置信度
    box_class_probs = K.sigmoid(feats[..., 5:])    #类别可能性

    if calc_loss == True:
        return grid, feats, box_xy, box_wh
    return box_xy, box_wh, box_confidence, box_class_probs


def yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape):
    '''
    Get corrected boxes
    获取更正后的检测框，即对识别结果的检测框进行更正
    '''
    box_yx = box_xy[..., ::-1]
    box_hw = box_wh[..., ::-1]
    input_shape = K.cast(input_shape, K.dtype(box_yx))    #输入形状
    image_shape = K.cast(image_shape, K.dtype(box_yx))    #图片形状
    new_shape = K.round(image_shape * K.min(input_shape/image_shape))    #按比例缩放图片
    offset = (input_shape-new_shape)/2./input_shape
    scale = input_shape/new_shape
    box_yx = (box_yx - offset) * scale
    box_hw *= scale

    box_mins = box_yx - (box_hw / 2.)
    box_maxes = box_yx + (box_hw / 2.)
    boxes =  K.concatenate([
        box_mins[..., 0:1],  # y_min
        box_mins[..., 1:2],  # x_min
        box_maxes[..., 0:1],  # y_max
        box_maxes[..., 1:2]  # x_max
    ])

    # Scale boxes back to original image shape.
    boxes *= K.concatenate([image_shape, image_shape])
    return boxes


def yolo_boxes_and_scores(feats, anchors, num_classes, input_shape, image_shape):
    '''
    Process Conv layer output
    处理卷积层的输出
    '''
    box_xy, box_wh, box_confidence, box_class_probs = yolo_head(feats,
        anchors, num_classes, input_shape)
    boxes = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape)
    boxes = K.reshape(boxes, [-1, 4])
    box_scores = box_confidence * box_class_probs
    box_scores = K.reshape(box_scores, [-1, num_classes])
    return boxes, box_scores


def yolo_eval(yolo_outputs,
              anchors,
              num_classes,
              image_shape,
              max_boxes=20,
              score_threshold=.6,
              iou_threshold=.5):
    """
    Evaluate YOLO model on given input and return filtered boxes.
    评估YOLO模型
    """
    num_layers = len(yolo_outputs)    #3
    anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]] # default setting
    input_shape = K.shape(yolo_outputs[0])[1:3] * 32
    boxes = []
    box_scores = []
    # 遍历YOLO模型的3个输出
    for l in range(num_layers):
        _boxes, _box_scores = yolo_boxes_and_scores(yolo_outputs[l],
            anchors[anchor_mask[l]], num_classes, input_shape, image_shape)
        boxes.append(_boxes)
        box_scores.append(_box_scores)
    boxes = K.concatenate(boxes, axis=0)
    box_scores = K.concatenate(box_scores, axis=0)

    mask = box_scores >= score_threshold    #掩码
    max_boxes_tensor = K.constant(max_boxes, dtype='int32')
    boxes_ = []
    scores_ = []
    classes_ = []
    # 遍历类别
    for c in range(num_classes):
        # TODO: use keras backend instead of tf.
        class_boxes = tf.boolean_mask(boxes, mask[:, c])    #对张量使用布尔掩码进行筛选
        class_box_scores = tf.boolean_mask(box_scores[:, c], mask[:, c])
        nms_index = tf.image.non_max_suppression(
            class_boxes, class_box_scores, max_boxes_tensor, iou_threshold=iou_threshold)
        class_boxes = K.gather(class_boxes, nms_index)
        class_box_scores = K.gather(class_box_scores, nms_index)
        classes = K.ones_like(class_box_scores, 'int32') * c
        boxes_.append(class_boxes)
        scores_.append(class_box_scores)
        classes_.append(classes)
    boxes_ = K.concatenate(boxes_, axis=0)
    scores_ = K.concatenate(scores_, axis=0)
    classes_ = K.concatenate(classes_, axis=0)

    return boxes_, scores_, classes_


def preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes):
    '''
    Preprocess true boxes to training input format
    处理训练数据集输入的标注框

    Parameters
    ----------
    true_boxes: array, shape=(m, T, 5)
        Absolute x_min, y_min, x_max, y_max, class_id relative to input_shape.
    input_shape: array-like, hw, multiples of 32
    anchors: array, shape=(N, 2), wh,标注框的宽高
    num_classes: integer

    Returns
    -------
    y_true: list of array, shape like yolo_outputs, xywh are reletive value

    '''
    assert (true_boxes[..., 4]<num_classes).all(), 'class id must be less than num_classes'
    num_layers = len(anchors)//3 # default setting
    anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]

    true_boxes = np.array(true_boxes, dtype='float32')
    input_shape = np.array(input_shape, dtype='int32')
    boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2
    boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]
    true_boxes[..., 0:2] = boxes_xy/input_shape[::-1]
    true_boxes[..., 2:4] = boxes_wh/input_shape[::-1]

    m = true_boxes.shape[0]
    grid_shapes = [input_shape//{0:32, 1:16, 2:8}[l] for l in range(num_layers)]
    y_true = [np.zeros((m,grid_shapes[l][0],grid_shapes[l][1],len(anchor_mask[l]),5+num_classes),
        dtype='float32') for l in range(num_layers)]

    # Expand dim to apply broadcasting.
    anchors = np.expand_dims(anchors, 0)
    anchor_maxes = anchors / 2.
    anchor_mins = -anchor_maxes
    valid_mask = boxes_wh[..., 0]>0

    for b in range(m):
        # Discard zero rows.
        wh = boxes_wh[b, valid_mask[b]]
        if len(wh)==0: continue
        # Expand dim to apply broadcasting.
        wh = np.expand_dims(wh, -2)
        box_maxes = wh / 2.
        box_mins = -box_maxes

        intersect_mins = np.maximum(box_mins, anchor_mins)
        intersect_maxes = np.minimum(box_maxes, anchor_maxes)
        intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.)
        intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
        box_area = wh[..., 0] * wh[..., 1]
        anchor_area = anchors[..., 0] * anchors[..., 1]
        iou = intersect_area / (box_area + anchor_area - intersect_area)

        # Find best anchor for each true box
        best_anchor = np.argmax(iou, axis=-1)

        for t, n in enumerate(best_anchor):
            for l in range(num_layers):
                if n in anchor_mask[l]:
                    i = np.floor(true_boxes[b,t,0]*grid_shapes[l][1]).astype('int32')
                    j = np.floor(true_boxes[b,t,1]*grid_shapes[l][0]).astype('int32')
                    k = anchor_mask[l].index(n)
                    c = true_boxes[b,t, 4].astype('int32')
                    y_true[l][b, j, i, k, 0:4] = true_boxes[b,t, 0:4]
                    y_true[l][b, j, i, k, 4] = 1
                    y_true[l][b, j, i, k, 5+c] = 1

    return y_true


def box_iou(b1, b2):
    '''
    Return iou tensor

    Parameters
    ----------
    b1: tensor, shape=(i1,...,iN, 4), xywh
    b2: tensor, shape=(j, 4), xywh

    Returns
    -------
    iou: tensor, shape=(i1,...,iN, j)

    '''

    # Expand dim to apply broadcasting.
    b1 = K.expand_dims(b1, -2)
    b1_xy = b1[..., :2]
    b1_wh = b1[..., 2:4]
    b1_wh_half = b1_wh/2.
    b1_mins = b1_xy - b1_wh_half
    b1_maxes = b1_xy + b1_wh_half

    # Expand dim to apply broadcasting.
    b2 = K.expand_dims(b2, 0)
    b2_xy = b2[..., :2]
    b2_wh = b2[..., 2:4]
    b2_wh_half = b2_wh/2.
    b2_mins = b2_xy - b2_wh_half
    b2_maxes = b2_xy + b2_wh_half

    intersect_mins = K.maximum(b1_mins, b2_mins)
    intersect_maxes = K.minimum(b1_maxes, b2_maxes)
    intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
    intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
    b1_area = b1_wh[..., 0] * b1_wh[..., 1]
    b2_area = b2_wh[..., 0] * b2_wh[..., 1]
    iou = intersect_area / (b1_area + b2_area - intersect_area)

    return iou


def yolo_loss(args, anchors, num_classes, ignore_thresh=.5, print_loss=False):
    '''
    Return yolo_loss tensor

    Parameters
    ----------
    yolo_outputs: list of tensor, the output of yolo_body or tiny_yolo_body
    y_true: list of array, the output of preprocess_true_boxes
    anchors: array, shape=(N, 2), wh
    num_classes: integer
    ignore_thresh: float, the iou threshold whether to ignore object confidence loss

    Returns
    -------
    loss: tensor, shape=(1,)

    '''
    num_layers = len(anchors)//3 # default setting 3聚类所得的标注框的数量不能改变
    yolo_outputs = args[:num_layers]
    y_true = args[num_layers:]
    anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]
    input_shape = K.cast(K.shape(yolo_outputs[0])[1:3] * 32, K.dtype(y_true[0]))
    grid_shapes = [K.cast(K.shape(yolo_outputs[l])[1:3], K.dtype(y_true[0])) for l in range(num_layers)]
    loss = 0
    m = K.shape(yolo_outputs[0])[0] # batch size, tensor
    mf = K.cast(m, K.dtype(yolo_outputs[0]))

    for l in range(num_layers):
        object_mask = y_true[l][..., 4:5]
        true_class_probs = y_true[l][..., 5:]

        grid, raw_pred, pred_xy, pred_wh = yolo_head(yolo_outputs[l],
             anchors[anchor_mask[l]], num_classes, input_shape, calc_loss=True)
        pred_box = K.concatenate([pred_xy, pred_wh])

        # Darknet raw box to calculate loss.
        raw_true_xy = y_true[l][..., :2]*grid_shapes[l][::-1] - grid
        raw_true_wh = K.log(y_true[l][..., 2:4] / anchors[anchor_mask[l]] * input_shape[::-1])
        raw_true_wh = K.switch(object_mask, raw_true_wh, K.zeros_like(raw_true_wh)) # avoid log(0)=-inf
        box_loss_scale = 2 - y_true[l][...,2:3]*y_true[l][...,3:4]

        # Find ignore mask, iterate over each of batch.
        ignore_mask = tf.TensorArray(K.dtype(y_true[0]), size=1, dynamic_size=True)
        object_mask_bool = K.cast(object_mask, 'bool')
        def loop_body(b, ignore_mask):
            true_box = tf.boolean_mask(y_true[l][b,...,0:4], object_mask_bool[b,...,0])
            iou = box_iou(pred_box[b], true_box)    #计算iou
            best_iou = K.max(iou, axis=-1)
            ignore_mask = ignore_mask.write(b, K.cast(best_iou<ignore_thresh, K.dtype(true_box)))
            return b+1, ignore_mask
        _, ignore_mask = K.control_flow_ops.while_loop(lambda b,*args: b<m, loop_body, [0, ignore_mask])
        ignore_mask = ignore_mask.stack()
        ignore_mask = K.expand_dims(ignore_mask, -1)

        # K.binary_crossentropy is helpful to avoid exp overflow.
        xy_loss = object_mask * box_loss_scale * K.binary_crossentropy(raw_true_xy, raw_pred[...,0:2], from_logits=True)
        wh_loss = object_mask * box_loss_scale * 0.5 * K.square(raw_true_wh-raw_pred[...,2:4])
        confidence_loss = object_mask * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True)+ \
            (1-object_mask) * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True) * ignore_mask
        class_loss = object_mask * K.binary_crossentropy(true_class_probs, raw_pred[...,5:], from_logits=True)

        xy_loss = K.sum(xy_loss) / mf
        wh_loss = K.sum(wh_loss) / mf
        confidence_loss = K.sum(confidence_loss) / mf
        class_loss = K.sum(class_loss) / mf
        loss += xy_loss + wh_loss + confidence_loss + class_loss
        if print_loss:
            loss = tf.Print(loss, [loss, xy_loss, wh_loss, confidence_loss, class_loss, K.sum(ignore_mask)], message='loss: ')
    return loss