RetinaFace-Pytorch源碼閱讀

論文：https://arxiv.org/pdf/1905.00641.pdf
代碼：https://github.com/deepinsight/insightface/tree/master/RetinaFace
Pytorch復現：https://github.com/biubug6/Pytorch_Retinaface

Pytorch真香~本文解讀代碼基於Pytorch復現版。關於RetinaFace理論知識請參考：RetinaFace論文解讀
先回顧RetinaFace的結構，如下：

以上，RetinaFace的結構主要由四個block組成，Backbone，FPN，Context Module，Loss計算四個部分，其中Context Module採用SSH結構。
所以本文也依據RetinaFace的結構閱讀其代碼。

注：該代碼的head沒有復現人臉像素的三維位置和對應關係這部分（說實話這部分看不太明白。。。），所以只關注人臉分類，框迴歸，landmark迴歸即可

Backbone
該復現代碼使用MobileNetV1作爲Backbone，如下：

class MobileNetV1(nn.Module):
    def __init__(self):
        super(MobileNetV1, self).__init__()
        ''' stage1階段進行三次下采樣 '''
        self.stage1 = nn.Sequential(
            conv_bn(3, 8, 2, leaky = 0.1),    # 3
            conv_dw(8, 16, 1),   # 7
            conv_dw(16, 32, 2),  # 11
            conv_dw(32, 32, 1),  # 19
            conv_dw(32, 64, 2),  # 27
            conv_dw(64, 64, 1),  # 43
        )
        ''' stage2 進行一次下采樣'''
        self.stage2 = nn.Sequential(
            conv_dw(64, 128, 2),  # 43 + 16 = 59
            conv_dw(128, 128, 1), # 59 + 32 = 91
            conv_dw(128, 128, 1), # 91 + 32 = 123
            conv_dw(128, 128, 1), # 123 + 32 = 155
            conv_dw(128, 128, 1), # 155 + 32 = 187
            conv_dw(128, 128, 1), # 187 + 32 = 219
        )
        ''' stage3進行一次下采樣'''
        self.stage3 = nn.Sequential(
            conv_dw(128, 256, 2), # 219 +3 2 = 241
            conv_dw(256, 256, 1), # 241 + 64 = 301
        )
        self.avg = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Linear(256, 1000)

    def forward(self, x):
        x = self.stage1(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.avg(x)
        # x = self.model(x)
        x = x.view(-1, 256)
        x = self.fc(x)
        return x

FPN

class FPN(nn.Module):
    def __init__(self,in_channels_list,out_channels):
        super(FPN,self).__init__()
        leaky = 0
        if (out_channels <= 64):
            leaky = 0.1
        self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride = 1, leaky = leaky)
        self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride = 1, leaky = leaky)
        self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride = 1, leaky = leaky)

        self.merge1 = conv_bn(out_channels, out_channels, leaky = leaky)
        self.merge2 = conv_bn(out_channels, out_channels, leaky = leaky)

    def forward(self, input):
        # names = list(input.keys())
        input = list(input.values())

        output1 = self.output1(input[0])
        output2 = self.output2(input[1])
        output3 = self.output3(input[2])
		''' 最後一層stage3的輸出output3進行上採樣，與stage2輸出merge，形成output2'''
        up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode="nearest")
        output2 = output2 + up3
        output2 = self.merge2(output2)
		''' output2進行上採樣與stage1的輸出進行merge，形成output1'''
        up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode="nearest")
        output1 = output1 + up2
        output1 = self.merge1(output1)
	
        out = [output1, output2, output3]
        return out

SSH

class SSH(nn.Module):
    def __init__(self, in_channel, out_channel):
        super(SSH, self).__init__()
        assert out_channel % 4 == 0
        leaky = 0
        if (out_channel <= 64):
            leaky = 0.1
        self.conv3X3 = conv_bn_no_relu(in_channel, out_channel//2, stride=1)

        self.conv5X5_1 = conv_bn(in_channel, out_channel//4, stride=1, leaky = leaky)
        self.conv5X5_2 = conv_bn_no_relu(out_channel//4, out_channel//4, stride=1)

        self.conv7X7_2 = conv_bn(out_channel//4, out_channel//4, stride=1, leaky = leaky)
        self.conv7x7_3 = conv_bn_no_relu(out_channel//4, out_channel//4, stride=1)

    def forward(self, input):
        conv3X3 = self.conv3X3(input)

        conv5X5_1 = self.conv5X5_1(input)
        conv5X5 = self.conv5X5_2(conv5X5_1)

        conv7X7_2 = self.conv7X7_2(conv5X5_1)
        conv7X7 = self.conv7x7_3(conv7X7_2)

        out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)
        out = F.relu(out)
        return out

定義了以上的Backbone，FPN，SSH，可以構建RetinaFace模型了。

RetinaFace

class RetinaFace(nn.Module):
    def __init__(self, cfg = None, phase = 'train'):
        """
        :param cfg:  Network related settings.
        :param phase: train or test.
        """
        super(RetinaFace,self).__init__()
        self.phase = phase
        backbone = None
        if cfg['name'] == 'mobilenet0.25':
            backbone = MobileNetV1()
            if cfg['pretrain']:
                checkpoint = torch.load("./weights/mobilenetV1X0.25_pretrain.tar", map_location=torch.device('cpu'))
                from collections import OrderedDict
                new_state_dict = OrderedDict()
                for k, v in checkpoint['state_dict'].items():
                    name = k[7:]  # remove module.
                    new_state_dict[name] = v
                # load params
                backbone.load_state_dict(new_state_dict)
        elif cfg['name'] == 'Resnet50':
            import torchvision.models as models
            backbone = models.resnet50(pretrained=cfg['pretrain'])

        self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])
        in_channels_stage2 = cfg['in_channel']
        in_channels_list = [
            in_channels_stage2 * 2,
            in_channels_stage2 * 4,
            in_channels_stage2 * 8,
        ]
        out_channels = cfg['out_channel']
        self.fpn = FPN(in_channels_list,out_channels)
        self.ssh1 = SSH(out_channels, out_channels)
        self.ssh2 = SSH(out_channels, out_channels)
        self.ssh3 = SSH(out_channels, out_channels)

        self.ClassHead = self._make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
        self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
        self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])

    def _make_class_head(self,fpn_num=3,inchannels=64,anchor_num=2):
        classhead = nn.ModuleList()
        for i in range(fpn_num):
            classhead.append(ClassHead(inchannels,anchor_num))
        return classhead
    
    def _make_bbox_head(self,fpn_num=3,inchannels=64,anchor_num=2):
        bboxhead = nn.ModuleList()
        for i in range(fpn_num):
            bboxhead.append(BboxHead(inchannels,anchor_num))
        return bboxhead

    def _make_landmark_head(self,fpn_num=3,inchannels=64,anchor_num=2):
        landmarkhead = nn.ModuleList()
        for i in range(fpn_num):
            landmarkhead.append(LandmarkHead(inchannels,anchor_num))
        return landmarkhead
	''' 構建RetinaFace模型'''
    def forward(self,inputs):
    	''' self.body 是mobilenetv1'''
        out = self.body(inputs)

        ''' self.fpn 是 fpn'''
        fpn = self.fpn(out)

        ''' self.ssh 是 ssh'''
        feature1 = self.ssh1(fpn[0])
        feature2 = self.ssh2(fpn[1])
        feature3 = self.ssh3(fpn[2])
        features = [feature1, feature2, feature3]

        bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
        classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)],dim=1)
        ldm_regressions = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=1)

        if self.phase == 'train':
            output = (bbox_regressions, classifications, ldm_regressions)
        else:
            output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
        return output

下面根據代碼直觀展示RetinaFace的整體結構，我借鑑大佬@茴香豆502的圖片，如有侵權，請聯繫刪除

附上代碼裏打印出來的網絡結構如下：

RetinaFace(
''' body '''
  (body): IntermediateLayerGetter(
    (stage1): Sequential(
      (0): Sequential(
        (0): Conv2d(3, 8, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
      )
      (1): Sequential(
        (0): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=8, bias=False)
        (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
      (2): Sequential(
        (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=16, bias=False)
        (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(16, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
      (3): Sequential(
        (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
      (4): Sequential(
        (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
      (5): Sequential(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
    )
    (stage2): Sequential(
      (0): Sequential(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=64, bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
      (1): Sequential(
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
      (2): Sequential(
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
      (3): Sequential(
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
      (4): Sequential(
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
      (5): Sequential(
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
    )
    (stage3): Sequential(
      (0): Sequential(
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=128, bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
      (1): Sequential(
        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.1, inplace=True)
        (3): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): LeakyReLU(negative_slope=0.1, inplace=True)
      )
    )
  )
  ''' fpn '''
  (fpn): FPN(
    (output1): Sequential(
      (0): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (output2): Sequential(
      (0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (output3): Sequential(
      (0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (merge1): Sequential(
      (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (merge2): Sequential(
      (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.1, inplace=True)
    )
  )
  ''' ssh '''
  (ssh1): SSH(
    (conv3X3): Sequential(
      (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (conv5X5_1): Sequential(
      (0): Conv2d(64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (conv5X5_2): Sequential(
      (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (conv7X7_2): Sequential(
      (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (conv7x7_3): Sequential(
      (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (ssh2): SSH(
    (conv3X3): Sequential(
      (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (conv5X5_1): Sequential(
      (0): Conv2d(64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (conv5X5_2): Sequential(
      (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (conv7X7_2): Sequential(
      (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (conv7x7_3): Sequential(
      (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (ssh3): SSH(
    (conv3X3): Sequential(
      (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (conv5X5_1): Sequential(
      (0): Conv2d(64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (conv5X5_2): Sequential(
      (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (conv7X7_2): Sequential(
      (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (conv7x7_3): Sequential(
      (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  ''' ClassHead '''
  (ClassHead): ModuleList(
    (0): ClassHead(
      (conv1x1): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1))
    )
    (1): ClassHead(
      (conv1x1): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1))
    )
    (2): ClassHead(
      (conv1x1): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1))
    )
  )
  ''' BboxHead '''
  (BboxHead): ModuleList(
    (0): BboxHead(
      (conv1x1): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))
    )
    (1): BboxHead(
      (conv1x1): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))
    )
    (2): BboxHead(
      (conv1x1): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))
    )
  )
   ''' LandmarkHead '''
  (LandmarkHead): ModuleList(
    (0): LandmarkHead(
      (conv1x1): Conv2d(64, 20, kernel_size=(1, 1), stride=(1, 1))
    )
    (1): LandmarkHead(
      (conv1x1): Conv2d(64, 20, kernel_size=(1, 1), stride=(1, 1))
    )
    (2): LandmarkHead(
      (conv1x1): Conv2d(64, 20, kernel_size=(1, 1), stride=(1, 1))
    )
  )
)

參考
https://editor.csdn.net/md?articleId=106363088
https://github.com/biubug6/Pytorch_Retinaface