    def __init__(self,
                 in_channels=[1024, 512, 256],
                 anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
                          [59, 119], [116, 90], [156, 198], [373, 326]],
                 anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
                 num_classes=80,
                 loss='YOLOv3Loss',
                 iou_aware=False,
                 iou_aware_factor=0.4,
                 data_format='NCHW'):
        """
        Head for YOLOv3 network
        Args:
            num_classes (int): number of foreground classes
            anchors (list): anchors
            anchor_masks (list): anchor masks
            loss (object): YOLOv3Loss instance
            iou_aware (bool): whether to use iou_aware
            iou_aware_factor (float): iou aware factor
            data_format (str): data format, NCHW or NHWC
        """
        super(YOLOv3Head, self).__init__()
        assert len(in_channels) > 0, "in_channels length should > 0"
        self.in_channels = in_channels
        self.num_classes = num_classes
        self.loss = loss

        self.iou_aware = iou_aware
        self.iou_aware_factor = iou_aware_factor

        self.parse_anchor(anchors, anchor_masks)
        self.num_outputs = len(self.anchors)
        self.data_format = data_format

        # self.yolo_outputs = []
        self.yolo_outputs = nn.ModuleList()
        self.yolo_outputs_names = []
        for i in range(len(self.anchors)):

            if self.iou_aware:
                num_filters = len(self.anchors[i]) * (self.num_classes + 6)
            else:
                num_filters = len(self.anchors[i]) * (self.num_classes + 5)
            name = 'yolo_output_{}'.format(i)
            # conv = nn.Conv2D(
            #     in_channels=self.in_channels[i],
            #     out_channels=num_filters,
            #     kernel_size=1,
            #     stride=1,
            #     padding=0,
            #     data_format=data_format,
            #     bias_attr=ParamAttr(regularizer=L2Decay(0.)))
            conv = nn.Conv2d(
                in_channels=self.in_channels[i],
                out_channels=num_filters,
                kernel_size=1,
                stride=1,
                padding=0,
            )
            conv.skip_quant = True
            # yolo_output = self.add_sublayer(name, conv)
            # self.yolo_outputs.append(yolo_output)

            # self.yolo_outputs.append(conv)
            self.yolo_outputs.add_module(name, conv)
            self.yolo_outputs_names.append(name)
Head for YOLOv3 network
Args:
num_classes (int): number of foreground classes
anchors (list): anchors
anchor_masks (list): anchor masks
loss (object): YOLOv3Loss instance
iou_aware (bool): whether to use iou_aware
iou_aware_factor (float): iou aware factor
data_format (str): data format, NCHW or NHWC
ptstructure/layout/ptppyolov2/ppyolov2_yolo_head.py