RefineDet：（1）训练脚本解析

不念不忘少年蓝@ 2022-02-04 15:06 232阅读 0赞

**相关链接：**

1.  [《RefineDet：Single-Shot Refinement Neural Network for Object Detection》论文笔记][RefineDet_Single-Shot Refinement Neural Network for Object Detection]
2.  [RefineDet：（2）检测部分网络解析][RefineDet_2]
3.  [RefineDet：（3）C++测试代码][RefineDet_3_C]

# 1. 概述 #

RefineDet的相关原理介绍已经在之前的博客中做了介绍：  
这里主要就所用到的训练脚本中做一些分析，这里选用的网络是VGG16作为基础训练网络，相关训练脚本的参数解释如下

''' 所用文件位于：RefineDet-master\examples\refinedet\VGG16_VOC2007_512.py '''
    
    # Add extra layers on top of a "base" network (e.g. VGGNet or ResNet).
    # AddExtraLayers函数是网络结构构造中比较重要的函数，主要实现的就是论文中的
    # transfer connection block (TCB)部分，也就是类似FPN算法的特征融合操作。
    def AddExtraLayers(net, use_batchnorm=True, arm_source_layers=[], normalizations=[], lr_mult=1):
        use_relu = True
    
        # Add additional convolutional layers.
    	# 添加论文中所说的后面几层卷积
        # 512/32: 16 x 16
        from_layer = net.keys()[-1]
    
        # 512/64: 8 x 8
        out_layer = "conv6_1"
        ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 1, 0, 1, lr_mult=lr_mult)
    
        from_layer = out_layer
        out_layer = "conv6_2"
        ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 512, 3, 1, 2, lr_mult=lr_mult)
    	
    	# 按照指定的ARM特征图构建FPN网络结构
        arm_source_layers.reverse()  # ARM模块的特征图来源层
        normalizations.reverse()
        num_p = 6
        for index, layer in enumerate(arm_source_layers):
            out_layer = layer
    		# 对conv4_3与conv5_3层归一化操作，scale分别为10与8
    		# 文中讲到这两个层由于之前层的特征差异，这里对其归一化，并且不断学习scale
            if normalizations:
                if normalizations[index] != -1:
                    norm_name = "{}_norm".format(layer)
                    net[norm_name] = L.Normalize(net[layer], scale_filler=dict(type="constant", value=normalizations[index]),
                        across_spatial=False, channel_shared=False)
                    out_layer = norm_name
                    arm_source_layers[index] = norm_name
            from_layer = out_layer
            out_layer = "TL{}_{}".format(num_p, 1)
            ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 1, 1, lr_mult=lr_mult)
    
            if num_p == 6:  # 网络的最后一个TCB模块，没有后面的输入
                from_layer = out_layer
                out_layer = "TL{}_{}".format(num_p, 2)
                ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 1, 1, lr_mult=lr_mult)
    
                from_layer = out_layer
                out_layer = "P{}".format(num_p)
                ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 1, 1, lr_mult=lr_mult)
            else:  # 其它TCB模块的创建
                from_layer = out_layer
                out_layer = "TL{}_{}".format(num_p, 2)
                ConvBNLayer(net, from_layer, out_layer, use_batchnorm, False, 256, 3, 1, 1, lr_mult=lr_mult)
    
                from_layer = "P{}".format(num_p+1)
                out_layer = "P{}-up".format(num_p+1)
                DeconvBNLayer(net, from_layer, out_layer, use_batchnorm, False, 256, 2, 0, 2, lr_mult=lr_mult)
    
                from_layer = ["TL{}_{}".format(num_p, 2), "P{}-up".format(num_p+1)]
                out_layer = "Elt{}".format(num_p)
                EltwiseLayer(net, from_layer, out_layer)
                relu_name = '{}_relu'.format(out_layer)
                net[relu_name] = L.ReLU(net[out_layer], in_place=True)
                out_layer = relu_name
    
                from_layer = out_layer
                out_layer = "P{}".format(num_p)
                ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 1, 1, lr_mult=lr_mult)
    
            num_p = num_p - 1
    
        return net
    
    
    ### Modify the following parameters accordingly ###
    # The directory which contains the caffe code.
    # We assume you are running the script at the CAFFE_ROOT.
    caffe_root = os.getcwd()
    
    # Set true if you want to start training right after generating all files.
    run_soon = True
    # Set true if you want to load from most recently saved snapshot.
    # Otherwise, we will load from the pretrain_model defined below.
    resume_training = True  # 是否需要从上次训练中断的地方继续训练
    # If true, Remove old model files.
    remove_old_models = False  # 是否删除老的模型
    
    # The database file for training data. Created by data/VOC0712/create_data.sh
    train_data = "examples/VOC0712/VOC0712_trainval_lmdb"
    # The database file for testing data. Created by data/VOC0712/create_data.sh
    test_data = "examples/VOC0712/VOC0712_test_lmdb"
    # Specify the batch sampler.
    resize_width = 512  # 训练时候图像需要resize到的宽度
    resize_height = 512 # 训练时候图像需要resize到的高度
    resize = "{}x{}".format(resize_width, resize_height)
    # batch_sampler列表用在数据读取和处理中
    batch_sampler = [
            { 
                    'sampler': { 
                            },
                    'max_trials': 1,
                    'max_sample': 1,
            },
            { 
                    'sampler': { 
                            'min_scale': 0.3,
                            'max_scale': 1.0,
                            'min_aspect_ratio': 0.5,
                            'max_aspect_ratio': 2.0,
                            },
                    'sample_constraint': { 
                            'min_jaccard_overlap': 0.1,
                            },
                    'max_trials': 50,
                    'max_sample': 1,
            },
            { 
                    'sampler': { 
                            'min_scale': 0.3,
                            'max_scale': 1.0,
                            'min_aspect_ratio': 0.5,
                            'max_aspect_ratio': 2.0,
                            },
                    'sample_constraint': { 
                            'min_jaccard_overlap': 0.3,
                            },
                    'max_trials': 50,
                    'max_sample': 1,
            },
            { 
                    'sampler': { 
                            'min_scale': 0.3,
                            'max_scale': 1.0,
                            'min_aspect_ratio': 0.5,
                            'max_aspect_ratio': 2.0,
                            },
                    'sample_constraint': { 
                            'min_jaccard_overlap': 0.5,
                            },
                    'max_trials': 50,
                    'max_sample': 1,
            },
            { 
                    'sampler': { 
                            'min_scale': 0.3,
                            'max_scale': 1.0,
                            'min_aspect_ratio': 0.5,
                            'max_aspect_ratio': 2.0,
                            },
                    'sample_constraint': { 
                            'min_jaccard_overlap': 0.7,
                            },
                    'max_trials': 50,
                    'max_sample': 1,
            },
            { 
                    'sampler': { 
                            'min_scale': 0.3,
                            'max_scale': 1.0,
                            'min_aspect_ratio': 0.5,
                            'max_aspect_ratio': 2.0,
                            },
                    'sample_constraint': { 
                            'min_jaccard_overlap': 0.9,
                            },
                    'max_trials': 50,
                    'max_sample': 1,
            },
            { 
                    'sampler': { 
                            'min_scale': 0.3,
                            'max_scale': 1.0,
                            'min_aspect_ratio': 0.5,
                            'max_aspect_ratio': 2.0,
                            },
                    'sample_constraint': { 
                            'max_jaccard_overlap': 1.0,
                            },
                    'max_trials': 50,
                    'max_sample': 1,
            },
            ]
    # train_transform_param字典是对训练数据的预处理操作，数据增强
    train_transform_param = { 
            'mirror': True,  # 水平镜像
            'mean_value': [104, 117, 123],  # 图像均值
            'resize_param': {   # resize方法设置
                    'prob': 1,
                    'resize_mode': P.Resize.WARP,
                    'height': resize_height,
                    'width': resize_width,
                    'interp_mode': [
                            P.Resize.LINEAR,
                            P.Resize.AREA,
                            P.Resize.NEAREST,
                            P.Resize.CUBIC,
                            P.Resize.LANCZOS4,
                            ],
                    },
            'distort_param': {   # 明暗色彩等图像变换
                    'brightness_prob': 0.5,
                    'brightness_delta': 32,
                    'contrast_prob': 0.5,
                    'contrast_lower': 0.5,
                    'contrast_upper': 1.5,
                    'hue_prob': 0.5,
                    'hue_delta': 18,
                    'saturation_prob': 0.5,
                    'saturation_lower': 0.5,
                    'saturation_upper': 1.5,
                    'random_order_prob': 0.0,
                    },
            'expand_param': { 
                    'prob': 0.5,
                    'max_expand_ratio': 4.0,
                    },
            'emit_constraint': { 
                'emit_type': caffe_pb2.EmitConstraint.CENTER,
                }
            }
    # 网络测试时的参数设置
    test_transform_param = { 
            'mean_value': [104, 117, 123],
            'resize_param': { 
                    'prob': 1,
                    'resize_mode': P.Resize.WARP,
                    'height': resize_height,
                    'width': resize_width,
                    'interp_mode': [P.Resize.LINEAR],
                    },
            }
    
    # If true, use batch norm for all newly added layers.
    # Currently only the non batch norm version has been tested.
    # 设置batchnorm，默认为false
    use_batchnorm = False
    lr_mult = 1
    # Use different initial learning rate.
    # 初始学习率
    if use_batchnorm:
        base_lr = 0.0004
    else:
        # A learning rate for batch_size = 1, num_gpus = 1.
        base_lr = 0.00004
    
    # Modify the job name if you want.
    job_name = "refinedet_vgg16_{}".format(resize)
    # The name of the model. Modify it if you want.
    model_name = "VOC0712_{}".format(job_name)
    
    # Directory which stores the model .prototxt file.
    # 网络的*.prototxt文件存储路径
    save_dir = "models/VGGNet/VOC0712/{}".format(job_name)
    # Directory which stores the snapshot of models.
    # 网络训练的snapshot路径
    snapshot_dir = "models/VGGNet/VOC0712/{}".format(job_name)
    # Directory which stores the job script and log file.
    # job路径，存储网络文件、训练log等
    job_dir = "jobs/VGGNet/VOC0712/{}".format(job_name)
    # Directory which stores the detection results. 检测结果保存路径
    output_result_dir = "{}/data/RefineDet/pascal/VOCdevkit/results/VOC2007/{}/Main".format(os.environ['HOME'], job_name)
    
    # model definition files.
    train_net_file = "{}/train.prototxt".format(save_dir)
    test_net_file = "{}/test.prototxt".format(save_dir)
    deploy_net_file = "{}/deploy.prototxt".format(save_dir)
    solver_file = "{}/solver.prototxt".format(save_dir)
    # snapshot prefix.
    # 模型存储名字前缀
    snapshot_prefix = "{}/{}".format(snapshot_dir, model_name)
    # job script path.
    job_file = "{}/{}.sh".format(job_dir, model_name)
    
    # Stores the test image names and sizes. Created by data/VOC0712/create_list.sh
    # 测试文件
    name_size_file = "data/VOC0712/test_name_size.txt"
    # The pretrained model. We use the Fully convolutional reduced (atrous) VGGNet.
    # VGG的预训练模型路径
    pretrain_model = "models/VGGNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel"
    # Stores LabelMapItem.
    # 类别标签文件
    label_map_file = "data/VOC0712/labelmap_voc.prototxt"
    
    # MultiBoxLoss parameters.
    num_classes = 21		# 网络检测的类别数，类别+背景
    share_location = True
    background_label_id = 0
    train_on_diff_gt = True
    normalization_mode = P.Loss.VALID
    code_type = P.PriorBox.CENTER_SIZE
    ignore_cross_boundary_bbox = False
    mining_type = P.MultiBoxLoss.MAX_NEGATIVE  # 困难样本挖掘的方法，MAX和OHEM
    neg_pos_ratio = 3.  # 困难样本挖掘确定样本个数的相乘因子
    loc_weight = (neg_pos_ratio + 1.) / 4.  # 权值系数
    # 坐标回归的损失函数， 'conf_loss_type'是分类的损失函数
    multibox_loss_param = { 
        'loc_loss_type': P.MultiBoxLoss.SMOOTH_L1,
        'conf_loss_type': P.MultiBoxLoss.SOFTMAX,
        'loc_weight': loc_weight,
        'num_classes': num_classes,
        'share_location': share_location,
        'match_type': P.MultiBoxLoss.PER_PREDICTION,
        'overlap_threshold': 0.5,
        'use_prior_for_matching': True,
        'background_label_id': background_label_id,
        'use_difficult_gt': train_on_diff_gt,
        'mining_type': mining_type,
        'neg_pos_ratio': neg_pos_ratio,
        'neg_overlap': 0.5,
        'code_type': code_type,
        'ignore_cross_boundary_bbox': ignore_cross_boundary_bbox,
        'objectness_score': 0.01,
        }
    loss_param = { 
        'normalization': normalization_mode,
        }
    
    # parameters for generating priors.
    # minimum dimension of input image
    # min_dim = 512
    # conv4_3 ==> 64 x 64
    # conv5_3 ==> 32 x 32
    # fc7 ==> 16 x 16
    # conv6_2 ==> 8 x 8
    # ARM模块参与TCB的特征层
    arm_source_layers = ['conv4_3', 'conv5_3', 'fc7', 'conv6_2']
    # ODM模块的特征融合层
    odm_source_layers = ['P3', 'P4', 'P5', 'P6']
    # anchor的最小scale参数，为各层stride的4倍
    min_sizes = [32, 64, 128, 256]
    max_sizes = [[], [], [], []]
    # 为各层stride的参数
    steps = [8, 16, 32, 64]
    # anchor生成的长宽比例，prior_box_layer中由此生成[0.5,1.0,2.0]三种尺寸的anchor
    aspect_ratios = [[2], [2], [2], [2]]
    # L2 normalize conv4_3 and conv5_3.
    # 特征的归一化初始参数
    normalizations = [10, 8, -1, -1]
    # variance used to encode/decode prior bboxes.
    if code_type == P.PriorBox.CENTER_SIZE:
      prior_variance = [0.1, 0.1, 0.2, 0.2]
    else:
      prior_variance = [0.1]
    flip = True  # 是否翻转，比如anchor ratio的翻转
    clip = False # 边界剪裁
    
    # Solver parameters.
    # Defining which GPUs to use.
    # 使用GPU时所用设备设置
    gpus = "0,1,2,3"
    gpulist = gpus.split(",")
    num_gpus = len(gpulist)
    
    # Divide the mini-batch to different GPUs.
    # 训练时候每张卡上训练图片数目设置
    batch_size = 32
    accum_batch_size = 32
    iter_size = accum_batch_size / batch_size
    solver_mode = P.Solver.CPU
    device_id = 0
    batch_size_per_device = batch_size
    if num_gpus > 0:
      batch_size_per_device = int(math.ceil(float(batch_size) / num_gpus))
      iter_size = int(math.ceil(float(accum_batch_size) / (batch_size_per_device * num_gpus)))
      solver_mode = P.Solver.GPU
      device_id = int(gpulist[0])
    
    # 根据Loss设置基础学习率，默认为P.Loss.VALID，因而默认学习率为0.001
    if normalization_mode == P.Loss.NONE:
      base_lr /= batch_size_per_device
    elif normalization_mode == P.Loss.VALID:
      base_lr *= 25. / loc_weight
    elif normalization_mode == P.Loss.FULL:
      # Roughly there are 2000 prior bboxes per image.
      # TODO(weiliu89): Estimate the exact # of priors.
      base_lr *= 2000.
    
    # Evaluate on whole test set.
    # 测试网络相关参数设置，训练时网络只有train没有test
    num_test_image = 4952
    test_batch_size = 1
    test_iter = num_test_image / test_batch_size
    
    # solver参数配置
    solver_param = { 
        # Train parameters
        'base_lr': base_lr,
        'weight_decay': 0.0005,
        'lr_policy': "multistep",
        'stepvalue': [80000, 100000, 120000],
        'gamma': 0.1,
        'momentum': 0.9,
        'iter_size': iter_size,
        'max_iter': 120000,
        'snapshot': 5000,
        'display': 10,
        'average_loss': 10,
        'type': "SGD",
        'solver_mode': solver_mode,
        'device_id': device_id,
        'debug_info': False,
        'snapshot_after_train': True,
        # Test parameters
        # 'test_iter': [test_iter],
        # 'test_interval': 5000,
        # 'eval_type': "detection",
        # 'ap_version': "11point",
        # 'test_initialization': False,
        }
    
    # parameters for generating detection output.
    # 检测时候相关参数配置
    det_out_param = { 
        'num_classes': num_classes,  # 检测的类别数
        'share_location': share_location,
        'background_label_id': background_label_id,  # 背景类ID
        'nms_param': { 'nms_threshold': 0.45, 'top_k': 1000},  # NMS阈值与NMS之后保留的检测框个数
        'keep_top_k': 500,  # 最后网络输出时的检测框个数
        'confidence_threshold': 0.01, # NMS中设置的检测置信度阈值
        'code_type': code_type,
        'objectness_score': 0.01,  # ARM中是否为目标的置信度阈值
        }
    
    # parameters for evaluating detection results.
    # 进行评估时的参数设置
    det_eval_param = { 
        'num_classes': num_classes,
        'background_label_id': background_label_id,
        'overlap_threshold': 0.5,
        'evaluate_difficult_gt': False,
        'name_size_file': name_size_file,
        }
    
    ### Hopefully you don't need to change the following ###
    # Check file. 一些相关训练启动检查
    check_if_exist(train_data)
    check_if_exist(test_data)
    check_if_exist(label_map_file)
    check_if_exist(pretrain_model)
    make_if_not_exist(save_dir)
    make_if_not_exist(job_dir)
    make_if_not_exist(snapshot_dir)
    
    # Create train net.
    # 调用caffe.NetSpec()初始化得到一个网络
    net = caffe.NetSpec()
    # CreateAnnotatedDataLayer函数是用来读取数据的，函数所在脚本：
    # ~RefineDet/python/caffe/model_libs.py，这部分和SSD代码是一样的。
    net.data, net.label = CreateAnnotatedDataLayer(train_data, batch_size=batch_size_per_device,
            train=True, output_label=True, label_map_file=label_map_file,
            transform_param=train_transform_param, batch_sampler=batch_sampler)
    
    # VGG骨干网络创建
    VGGNetBody(net, from_layer='data', fully_conv=True, reduced=True, dilated=False, dropout=False)
    
    # AddExtraLayers函数是基于前面的得到VGG网络结构增加2个卷积层，
    # 然后对4个层执行论文中Figure1的transfer connection block操作。
    # 首先取VGG的conv4_3、conv5_3、fc7、conv6_2层输出，假设输入图像大小是320*320，
    # 那么这4层的输出feature map大小分别是40*40,20*20,10*10,5*5。
    # 这4层就对应Figure1中Anchor Refinement Module部分的4个灰色矩形块，
    # 接着从这4个矩形块引出Transfer Connection Block得到P6,P5,P4,P3，
    # 也就是Figure1中Object Detection Module部分的4个蓝色矩形块。
    # 这就是AddExtraLayers函数实现的内容。
    AddExtraLayers(net, use_batchnorm, arm_source_layers, normalizations, lr_mult=lr_mult)
    # 因为前面AddExtraLayers函数中对arm_source_layers执行了reverse操作，所以这里相当于再反转回来。
    arm_source_layers.reverse()
    normalizations.reverse()
    
    # CreateRefineDetHead函数用来生成分类层、回归层等，是比较重要的一个函数，
    # 经过该函数后返回的mbox_layers就是完整的网络结构输出，该函数所在脚本：
    # ~RefineDet/python/caffe/model_libs.py，这部分代码也是在原来SSD的
    # CreateMultiBoxHead函数基础上修改得到的。
    # 该函数有两个重要输入：from_layers=arm_source_layers和
    # from_layers2=odm_source_layers，前者是Figuer1中4个灰色矩形块的集合（arm是
    # Anchor Refinement Module的缩写）；后者是Figure1中4个蓝色矩形块的集合（odm是
    # Object Detection Module的缩写），初始化为['P3', 'P4', 'P5', 'P6']，
    # 这也是本文和SSD算法比较大的不同点。最后大概介绍下返回结果mbox_layers的内容：
    # mbox_layers[0]是"arm_loc"，表示bbox的回归输出；
    # mbox_layers[1]是"arm_conf"，表示bbox的分类输出（是否是object的二分类）；
    # mbox_layers[2]是"arm_priorbox"，表示priorbox（anchor）的信息；
    # mbox_layers[3]是”odm_conf“，表示bbox的回归输出；
    # mbox_layers[4]是”odm_loc“，表示bbox的分类输出（类别数是所有object的类别数+背景）。
    mbox_layers = CreateRefineDetHead(net, data_layer='data', from_layers=arm_source_layers,
            use_batchnorm=use_batchnorm, min_sizes=min_sizes, max_sizes=max_sizes,
            aspect_ratios=aspect_ratios, steps=steps, normalizations=[],
            num_classes=num_classes, share_location=share_location, flip=flip, clip=clip,
            prior_variance=prior_variance, kernel_size=3, pad=1, lr_mult=lr_mult, from_layers2=odm_source_layers)
    
    # 定义好网络结构后，就要定义损失函数了。
    # 先定义”arm_loss“，通过L.MultiBoxLoss接口来计算损失函数。mbox_layers_arm列表
    # 就是保存了bbox的回归输出（”arm_loc“）、分类输出（”arm_conf“）、anchor（或者叫priorbox）
    # 信息（”arm_priorbox“）、ground truth信息（net.label），multibox_loss_param_arm中
    # 除了分类类别数（”num_classes=2“）与原来的配置不同外，其他都是沿用原来的配置。
    # 回传损失只回传前面两个变量。这部分损失基本上和RPN网络类似，分类部分的损失根据
    # mbox_layers[1]（”arm_conf“）和net.label来得到，回归部分的损失根据
    # mbox_layers[0]（”arm_loc“）和mbox_layers[2]（”arm_priorbox“）来得到。
    # MultiBoxLoss是自定义层，最早见于SSD算法中，这里稍作修改，源码参考
    # https://github.com/sfzhang15/RefineDet/blob/master/src/caffe/layers/multibox_loss_layer.cpp。
    # 不过在这里调用时候和在SSD中调用没有区别（输入列表都是4个变量），只不过分类的类别数变化了而已。
    name = "arm_loss"
    mbox_layers_arm = []
    mbox_layers_arm.append(mbox_layers[0]) # mbox_layers[0]是"arm_loc"，表示bbox的回归输出
    mbox_layers_arm.append(mbox_layers[1]) # mbox_layers[1]是"arm_conf"，表示bbox的分类输出（是否是object的二分类）
    mbox_layers_arm.append(mbox_layers[2]) # mbox_layers[2]是"arm_priorbox"，表示priorbox（anchor）的信息
    mbox_layers_arm.append(net.label)
    multibox_loss_param_arm = multibox_loss_param.copy()
    multibox_loss_param_arm['num_classes'] = 2
    net[name] = L.MultiBoxLoss(*mbox_layers_arm, multibox_loss_param=multibox_loss_param_arm,
            loss_param=loss_param, include=dict(phase=caffe_pb2.Phase.Value('TRAIN')),
            propagate_down=[True, True, False, False])
    
    # 这一部分代码主要是将net["arm_conf"]作为softmax函数的输入，并得到分类概率输出net[flatten_name]，
    # 或者写成net["arm_conf_flatten"]。net["arm_conf"]是前面arm部分的二分类输出结果，
    # 因此这部分操作和Faster RCNN中得到proposal的过程几乎是一样的。
    # Create the MultiBoxLossLayer.
    conf_name = "arm_conf"
    reshape_name = "{}_reshape".format(conf_name)
    net[reshape_name] = L.Reshape(net[conf_name], shape=dict(dim=[0, -1, 2]))
    softmax_name = "{}_softmax".format(conf_name)
    net[softmax_name] = L.Softmax(net[reshape_name], axis=2)
    flatten_name = "{}_flatten".format(conf_name)
    net[flatten_name] = L.Flatten(net[softmax_name], axis=1)
    
    # 定义”odm_loss“，也是通过L.MultiBoxLoss接口来计算损失函数。
    # mbox_layers_odm列表保存了bbox的回归输出（”odm_loc“）、分类输出（”odm_conf“）、
    # anchor（或者叫priorbox）信息（”arm_priorbox“）、gound truth信息（net.label）、
    # 分类的概率输出（net["arm_conf_flatten"]）、ARM部分的bbox的回归输出（net[”arm_loc“]）。
    # 除了前面两个变量外，后面4个变量都是为了做bbox的过滤和正负样本的平衡，因此回传损失只回传前面两个变量。
    # 这部分的损失函数计算和SSD算法类似，分类支路的损失根据mbox_layers[4]（”odm_conf“）和
    # net.label得到，回归支路的损失根据mbox_layers[3]（”odm_loc“）和
    # mbox_layers[2]（”arm_priorbox“）得到。这里可以看到输入列表变成6个变量，
    # 这里就是RefineNet中对MultiBoxLossLayer的修改：增加了两个输入变量。
    # 这部分非常重要，也是RefineDet的一个亮点的体现。这两个变量中的net["arm_conf_flatten"]
    # 主要参与到hard negative mining过程，是对负样本排序和sample的（文中说的是负样本
    # 的confidence（也就是判为负样本的概率）大于阈值0.99，则该样本不参与到ODM部分的训练）。
    # 另一个变量net[”arm_loc“]提供了bbox的初始坐标，有利于检测网络得到更准确的结果。
    name = "odm_loss"
    mbox_layers_odm = []
    mbox_layers_odm.append(mbox_layers[3]) # mbox_layers[3]是”odm_conf“，表示bbox的回归输出
    mbox_layers_odm.append(mbox_layers[4]) # mbox_layers[4]是”odm_loc“，表示bbox的分类输出（类别数是所有object的类别数+背景）
    mbox_layers_odm.append(mbox_layers[2]) # mbox_layers[2]是"arm_priorbox"，表示priorbox（anchor）的信息
    mbox_layers_odm.append(net.label)
    mbox_layers_odm.append(net[flatten_name])
    mbox_layers_odm.append(mbox_layers[0]) # mbox_layers[0]是"arm_loc"，表示bbox的回归输出
    net[name] = L.MultiBoxLoss(*mbox_layers_odm, multibox_loss_param=multibox_loss_param,
            loss_param=loss_param, include=dict(phase=caffe_pb2.Phase.Value('TRAIN')),
            propagate_down=[True, True, False, False, False, False])
    
    # 测试相关网络创建，网络定义文件导出等，就不作解释
    ......

# 2. Reference #

1.  [RefineDet算法源码 （一）训练脚本][RefineDet_]

[RefineDet_Single-Shot Refinement Neural Network for Object Detection]: https://blog.csdn.net/m_buddy/article/details/89932030
[RefineDet_2]: https://blog.csdn.net/m_buddy/article/details/89981053
[RefineDet_3_C]: https://blog.csdn.net/m_buddy/article/details/89981491
[RefineDet_]: https://blog.csdn.net/u014380165/article/details/80562416