根據?GitHub - RangiLyu/nanodet: NanoDet-Plus?Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥

打印調試得出

NanoDetPlus((backbone): ShuffleNetV2((conv1): Sequential((0): Conv2d(3, 24, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True))(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)(stage2): Sequential((0): ShuffleV2Block((branch1): Sequential((0): Conv2d(24, 24, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=24, bias=False)(1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Conv2d(24, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)(3): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(4): LeakyReLU(negative_slope=0.1, inplace=True))(branch2): Sequential((0): Conv2d(24, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(58, 58, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=58, bias=False)(4): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(1): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(58, 58, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=58, bias=False)(4): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(2): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(58, 58, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=58, bias=False)(4): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(3): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(58, 58, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=58, bias=False)(4): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True))))(stage3): Sequential((0): ShuffleV2Block((branch1): Sequential((0): Conv2d(116, 116, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=116, bias=False)(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(3): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(4): LeakyReLU(negative_slope=0.1, inplace=True))(branch2): Sequential((0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=116, bias=False)(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(1): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(2): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(3): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(4): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(5): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(6): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(7): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True))))(stage4): Sequential((0): ShuffleV2Block((branch1): Sequential((0): Conv2d(232, 232, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=232, bias=False)(1): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)(3): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(4): LeakyReLU(negative_slope=0.1, inplace=True))(branch2): Sequential((0): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(232, 232, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=232, bias=False)(4): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(1): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(232, 232, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=232, bias=False)(4): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(2): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(232, 232, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=232, bias=False)(4): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))(3): ShuffleV2Block((branch1): Sequential()(branch2): Sequential((0): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)(3): Conv2d(232, 232, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=232, bias=False)(4): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)(6): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(7): LeakyReLU(negative_slope=0.1, inplace=True)))))(fpn): GhostPAN((upsample): Upsample(scale_factor=2.0, mode=bilinear)(reduce_layers): ModuleList((0): ConvModule((conv): Conv2d(116, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(1): ConvModule((conv): Conv2d(232, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(2): ConvModule((conv): Conv2d(464, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True)))(top_down_blocks): ModuleList((0): GhostBlocks((blocks): Sequential((0): GhostBottleneck((ghost1): GhostModule((primary_conv): Sequential((0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True))(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)))(ghost2): GhostModule((primary_conv): Sequential((0): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential())(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential()))(shortcut): Sequential((0): Conv2d(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False)(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))))(1): GhostBlocks((blocks): Sequential((0): GhostBottleneck((ghost1): GhostModule((primary_conv): Sequential((0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True))(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)))(ghost2): GhostModule((primary_conv): Sequential((0): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential())(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential()))(shortcut): Sequential((0): Conv2d(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False)(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))))))(downsamples): ModuleList((0): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(1): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True)))(bottom_up_blocks): ModuleList((0): GhostBlocks((blocks): Sequential((0): GhostBottleneck((ghost1): GhostModule((primary_conv): Sequential((0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True))(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)))(ghost2): GhostModule((primary_conv): Sequential((0): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential())(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential()))(shortcut): Sequential((0): Conv2d(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False)(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))))(1): GhostBlocks((blocks): Sequential((0): GhostBottleneck((ghost1): GhostModule((primary_conv): Sequential((0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True))(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)))(ghost2): GhostModule((primary_conv): Sequential((0): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential())(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential()))(shortcut): Sequential((0): Conv2d(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False)(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))))))(extra_lvl_in_conv): ModuleList((0): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True)))(extra_lvl_out_conv): ModuleList((0): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))))(head): NanoDetPlusHead((distribution_project): Integral()(loss_qfl): QualityFocalLoss()(loss_dfl): DistributionFocalLoss()(loss_bbox): GIoULoss()(cls_convs): ModuleList((0): ModuleList((0): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(1): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True)))(1): ModuleList((0): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(1): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True)))(2): ModuleList((0): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(1): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True)))(3): ModuleList((0): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(1): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))))(gfl_cls): ModuleList((0): Conv2d(96, 52, kernel_size=(1, 1), stride=(1, 1))(1): Conv2d(96, 52, kernel_size=(1, 1), stride=(1, 1))(2): Conv2d(96, 52, kernel_size=(1, 1), stride=(1, 1))(3): Conv2d(96, 52, kernel_size=(1, 1), stride=(1, 1))))(aux_fpn): GhostPAN((upsample): Upsample(scale_factor=2.0, mode=bilinear)(reduce_layers): ModuleList((0): ConvModule((conv): Conv2d(116, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(1): ConvModule((conv): Conv2d(232, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(2): ConvModule((conv): Conv2d(464, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True)))(top_down_blocks): ModuleList((0): GhostBlocks((blocks): Sequential((0): GhostBottleneck((ghost1): GhostModule((primary_conv): Sequential((0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True))(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)))(ghost2): GhostModule((primary_conv): Sequential((0): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential())(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential()))(shortcut): Sequential((0): Conv2d(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False)(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))))(1): GhostBlocks((blocks): Sequential((0): GhostBottleneck((ghost1): GhostModule((primary_conv): Sequential((0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True))(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)))(ghost2): GhostModule((primary_conv): Sequential((0): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential())(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential()))(shortcut): Sequential((0): Conv2d(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False)(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))))))(downsamples): ModuleList((0): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(1): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True)))(bottom_up_blocks): ModuleList((0): GhostBlocks((blocks): Sequential((0): GhostBottleneck((ghost1): GhostModule((primary_conv): Sequential((0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True))(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)))(ghost2): GhostModule((primary_conv): Sequential((0): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential())(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential()))(shortcut): Sequential((0): Conv2d(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False)(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))))(1): GhostBlocks((blocks): Sequential((0): GhostBottleneck((ghost1): GhostModule((primary_conv): Sequential((0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True))(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): LeakyReLU(negative_slope=0.1, inplace=True)))(ghost2): GhostModule((primary_conv): Sequential((0): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential())(cheap_operation): Sequential((0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Sequential()))(shortcut): Sequential((0): Conv2d(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False)(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))))))(extra_lvl_in_conv): ModuleList((0): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True)))(extra_lvl_out_conv): ModuleList((0): DepthwiseConvModule((depthwise): Conv2d(96, 96, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=96, bias=False)(pointwise): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)(dwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pwnorm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))))(aux_head): SimpleConvHead((relu): ReLU(inplace=True)(cls_convs): ModuleList((0): ConvModule((conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 192, eps=1e-05, affine=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(1): ConvModule((conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 192, eps=1e-05, affine=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(2): ConvModule((conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 192, eps=1e-05, affine=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(3): ConvModule((conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 192, eps=1e-05, affine=True)(act): LeakyReLU(negative_slope=0.1, inplace=True)))(reg_convs): ModuleList((0): ConvModule((conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 192, eps=1e-05, affine=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(1): ConvModule((conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 192, eps=1e-05, affine=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(2): ConvModule((conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 192, eps=1e-05, affine=True)(act): LeakyReLU(negative_slope=0.1, inplace=True))(3): ConvModule((conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(gn): GroupNorm(32, 192, eps=1e-05, affine=True)(act): LeakyReLU(negative_slope=0.1, inplace=True)))(gfl_cls): Conv2d(192, 20, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(gfl_reg): Conv2d(192, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(scales): ModuleList((0): Scale()(1): Scale()(2): Scale()(3): Scale()))
)

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