输出网络结构图，mmdetection

控制台输入：python tools/train.py /home/yuan3080/桌面/detection_paper_6/mmdetection-master1/mmdetection-master_yanhuo/work_dirs/lad_r50_paa_r101_fpn_coco_1x/lad_r50_a_r101_fpn_coco_1x.py
这个是输出方法里面的，不是原始方法。
如下所示，加一个print(model)就可以
，然后运行：控制台输入
之后，之后输出即可，如下所示：
在这里插入图片描述
LAD((backbone): Res2Net((stem): Sequential((0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True)(6): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(8): ReLU(inplace=True))(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)(layer1): Res2Layer((0): Bottle2neck((conv1): Conv2d(64, 104, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(104, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(downsample): Sequential((0): AvgPool2d(kernel_size=1, stride=1, padding=0)(1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(convs): ModuleList((0): Conv2d(26, 26, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(26, 26, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(26, 26, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(26, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(26, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(26, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(1): Bottle2neck((conv1): Conv2d(256, 104, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(104, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(convs): ModuleList((0): Conv2d(26, 26, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(26, 26, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(26, 26, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(26, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(26, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(26, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(2): Bottle2neck((conv1): Conv2d(256, 104, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(104, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(convs): ModuleList((0): Conv2d(26, 26, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(26, 26, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(26, 26, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(26, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(26, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(26, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))))(layer2): Res2Layer((0): Bottle2neck((conv1): Conv2d(256, 208, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(208, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(downsample): Sequential((0): AvgPool2d(kernel_size=2, stride=2, padding=0)(1): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(pool): AvgPool2d(kernel_size=3, stride=2, padding=1)(convs): ModuleList((0): Conv2d(52, 52, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(1): Conv2d(52, 52, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(2): Conv2d(52, 52, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(1): Bottle2neck((conv1): Conv2d(512, 208, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(208, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(convs): ModuleList((0): Conv2d(52, 52, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(52, 52, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(52, 52, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(2): Bottle2neck((conv1): Conv2d(512, 208, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(208, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(convs): ModuleList((0): Conv2d(52, 52, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(52, 52, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(52, 52, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(3): Bottle2neck((conv1): Conv2d(512, 208, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(208, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(convs): ModuleList((0): Conv2d(52, 52, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(52, 52, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(52, 52, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))))(layer3): Res2Layer((0): Bottle2neck((conv1): Conv2d(512, 416, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(416, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(downsample): Sequential((0): AvgPool2d(kernel_size=2, stride=2, padding=0)(1): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)(2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(pool): AvgPool2d(kernel_size=3, stride=2, padding=1)(convs): ModuleList((0): Conv2d(104, 104, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(1): Conv2d(104, 104, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(2): Conv2d(104, 104, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(1): Bottle2neck((conv1): Conv2d(1024, 416, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(416, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(convs): ModuleList((0): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(2): Bottle2neck((conv1): Conv2d(1024, 416, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(416, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(convs): ModuleList((0): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(3): Bottle2neck((conv1): Conv2d(1024, 416, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(416, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(convs): ModuleList((0): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(4): Bottle2neck((conv1): Conv2d(1024, 416, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(416, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(convs): ModuleList((0): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(5): Bottle2neck((conv1): Conv2d(1024, 416, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(416, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(convs): ModuleList((0): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(104, 104, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))))(layer4): Res2Layer((0): Bottle2neck((conv1): Conv2d(1024, 832, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(832, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(downsample): Sequential((0): AvgPool2d(kernel_size=2, stride=2, padding=0)(1): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)(2): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))(pool): AvgPool2d(kernel_size=3, stride=2, padding=1)(convs): ModuleList((0): Conv2d(208, 208, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(1): Conv2d(208, 208, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(2): Conv2d(208, 208, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(1): Bottle2neck((conv1): Conv2d(2048, 832, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(832, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(convs): ModuleList((0): Conv2d(208, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(208, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(208, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(2): Bottle2neck((conv1): Conv2d(2048, 832, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(832, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(convs): ModuleList((0): Conv2d(208, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(1): Conv2d(208, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(2): Conv2d(208, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(bns): ModuleList((0): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))))init_cfg={'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}(neck): FPN((lateral_convs): ModuleList((0): ConvModule((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)))(1): ConvModule((conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1)))(2): ConvModule((conv): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))))(fpn_convs): ModuleList((0): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(1): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(2): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(3): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)))(4): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)))))init_cfg={'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}(bbox_head): LADHead((loss_cls): FocalLoss()(loss_bbox): GIoULoss()(relu): ReLU(inplace=True)(cls_convs): ModuleList((0): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 256, eps=1e-05, affine=True)(activate): ReLU(inplace=True))(1): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 256, eps=1e-05, affine=True)(activate): ReLU(inplace=True))(2): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 256, eps=1e-05, affine=True)(activate): ReLU(inplace=True))(3): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 256, eps=1e-05, affine=True)(activate): ReLU(inplace=True)))(reg_convs): ModuleList((0): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 256, eps=1e-05, affine=True)(activate): ReLU(inplace=True))(1): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 256, eps=1e-05, affine=True)(activate): ReLU(inplace=True))(2): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 256, eps=1e-05, affine=True)(activate): ReLU(inplace=True))(3): ConvModule((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 256, eps=1e-05, affine=True)(activate): ReLU(inplace=True)))(atss_cls): Conv2d(256, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(atss_reg): Conv2d(256, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(atss_centerness): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(scales): ModuleList((0): Scale()(1): Scale()(2): Scale()(3): Scale()(4): Scale())(loss_centerness): CrossEntropyLoss(avg_non_ignore=False))