EncoderDecoder(
  12.597 M, 100.000% Params, 11.767 GFLOPs, 100.000% FLOPs, 
  (backbone): STDCContextPathNet(
    11.71 M, 92.962% Params, 9.345 GFLOPs, 79.423% FLOPs, 
    (backbone): STDCNet(
      9.349 M, 74.215% Params, 7.275 GFLOPs, 61.826% FLOPs, 
      (stages): ModuleList(
        9.349 M, 74.215% Params, 7.275 GFLOPs, 61.826% FLOPs, 
        (0): ConvModule(
          0.001 M, 0.007% Params, 0.063 GFLOPs, 0.535% FLOPs, 
          (conv): Conv2d(0.001 M, 0.007% Params, 0.057 GFLOPs, 0.481% FLOPs, 3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.004 GFLOPs, 0.036% FLOPs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (activate): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.018% FLOPs, inplace=True)
        )
        (1): ConvModule(
          0.019 M, 0.147% Params, 0.305 GFLOPs, 2.593% FLOPs, 
          (conv): Conv2d(0.018 M, 0.146% Params, 0.302 GFLOPs, 2.567% FLOPs, 32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.002 GFLOPs, 0.018% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (activate): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.009% FLOPs, inplace=True)
        )
        (2): Sequential(
          0.515 M, 4.092% Params, 2.223 GFLOPs, 18.892% FLOPs, 
          (0): STDCModule(
            0.111 M, 0.885% Params, 0.565 GFLOPs, 4.803% FLOPs, 
            (layers): ModuleList(
              0.11 M, 0.874% Params, 0.557 GFLOPs, 4.736% FLOPs, 
              (0): ConvModule(
                0.008 M, 0.067% Params, 0.141 GFLOPs, 1.194% FLOPs, 
                (conv): Conv2d(0.008 M, 0.065% Params, 0.134 GFLOPs, 1.141% FLOPs, 64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.004 GFLOPs, 0.036% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.018% FLOPs, inplace=True)
              )
              (1): ConvModule(
                0.074 M, 0.586% Params, 0.303 GFLOPs, 2.573% FLOPs, 
                (conv): Conv2d(0.074 M, 0.585% Params, 0.302 GFLOPs, 2.567% FLOPs, 128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GFLOPs, 0.004% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.002% FLOPs, inplace=True)
              )
              (2): ConvModule(
                0.018 M, 0.147% Params, 0.076 GFLOPs, 0.645% FLOPs, 
                (conv): Conv2d(0.018 M, 0.146% Params, 0.075 GFLOPs, 0.642% FLOPs, 64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.002% FLOPs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (3): ConvModule(
                0.009 M, 0.074% Params, 0.038 GFLOPs, 0.324% FLOPs, 
                (conv): Conv2d(0.009 M, 0.073% Params, 0.038 GFLOPs, 0.321% FLOPs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.002% FLOPs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
            )
            (downsample): ConvModule(
              0.001 M, 0.011% Params, 0.006 GFLOPs, 0.049% FLOPs, 
              (conv): Conv2d(0.001 M, 0.009% Params, 0.005 GFLOPs, 0.040% FLOPs, 128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=128, bias=False)
              (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.001 GFLOPs, 0.009% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (skip): AvgPool2d(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.018% FLOPs, kernel_size=3, stride=2, padding=1)
          )
          (1): STDCModule(
            0.135 M, 1.069% Params, 0.553 GFLOPs, 4.696% FLOPs, 
            (layers): ModuleList(
              0.135 M, 1.069% Params, 0.553 GFLOPs, 4.696% FLOPs, 
              (0): ConvModule(
                0.033 M, 0.262% Params, 0.136 GFLOPs, 1.154% FLOPs, 
                (conv): Conv2d(0.033 M, 0.260% Params, 0.134 GFLOPs, 1.141% FLOPs, 256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.001 GFLOPs, 0.009% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
              )
              (1): ConvModule(
                0.074 M, 0.586% Params, 0.303 GFLOPs, 2.573% FLOPs, 
                (conv): Conv2d(0.074 M, 0.585% Params, 0.302 GFLOPs, 2.567% FLOPs, 128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GFLOPs, 0.004% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.002% FLOPs, inplace=True)
              )
              (2): ConvModule(
                0.018 M, 0.147% Params, 0.076 GFLOPs, 0.645% FLOPs, 
                (conv): Conv2d(0.018 M, 0.146% Params, 0.075 GFLOPs, 0.642% FLOPs, 64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.002% FLOPs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (3): ConvModule(
                0.009 M, 0.074% Params, 0.038 GFLOPs, 0.324% FLOPs, 
                (conv): Conv2d(0.009 M, 0.073% Params, 0.038 GFLOPs, 0.321% FLOPs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.002% FLOPs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
            )
          )
          (2): STDCModule(
            0.135 M, 1.069% Params, 0.553 GFLOPs, 4.696% FLOPs, 
            (layers): ModuleList(
              0.135 M, 1.069% Params, 0.553 GFLOPs, 4.696% FLOPs, 
              (0): ConvModule(
                0.033 M, 0.262% Params, 0.136 GFLOPs, 1.154% FLOPs, 
                (conv): Conv2d(0.033 M, 0.260% Params, 0.134 GFLOPs, 1.141% FLOPs, 256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.001 GFLOPs, 0.009% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
              )
              (1): ConvModule(
                0.074 M, 0.586% Params, 0.303 GFLOPs, 2.573% FLOPs, 
                (conv): Conv2d(0.074 M, 0.585% Params, 0.302 GFLOPs, 2.567% FLOPs, 128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GFLOPs, 0.004% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.002% FLOPs, inplace=True)
              )
              (2): ConvModule(
                0.018 M, 0.147% Params, 0.076 GFLOPs, 0.645% FLOPs, 
                (conv): Conv2d(0.018 M, 0.146% Params, 0.075 GFLOPs, 0.642% FLOPs, 64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.002% FLOPs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (3): ConvModule(
                0.009 M, 0.074% Params, 0.038 GFLOPs, 0.324% FLOPs, 
                (conv): Conv2d(0.009 M, 0.073% Params, 0.038 GFLOPs, 0.321% FLOPs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.002% FLOPs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
            )
          )
          (3): STDCModule(
            0.135 M, 1.069% Params, 0.553 GFLOPs, 4.696% FLOPs, 
            (layers): ModuleList(
              0.135 M, 1.069% Params, 0.553 GFLOPs, 4.696% FLOPs, 
              (0): ConvModule(
                0.033 M, 0.262% Params, 0.136 GFLOPs, 1.154% FLOPs, 
                (conv): Conv2d(0.033 M, 0.260% Params, 0.134 GFLOPs, 1.141% FLOPs, 256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.001 GFLOPs, 0.009% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
              )
              (1): ConvModule(
                0.074 M, 0.586% Params, 0.303 GFLOPs, 2.573% FLOPs, 
                (conv): Conv2d(0.074 M, 0.585% Params, 0.302 GFLOPs, 2.567% FLOPs, 128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GFLOPs, 0.004% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.002% FLOPs, inplace=True)
              )
              (2): ConvModule(
                0.018 M, 0.147% Params, 0.076 GFLOPs, 0.645% FLOPs, 
                (conv): Conv2d(0.018 M, 0.146% Params, 0.075 GFLOPs, 0.642% FLOPs, 64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.002% FLOPs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (3): ConvModule(
                0.009 M, 0.074% Params, 0.038 GFLOPs, 0.324% FLOPs, 
                (conv): Conv2d(0.009 M, 0.073% Params, 0.038 GFLOPs, 0.321% FLOPs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.002% FLOPs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
            )
          )
        )
        (3): Sequential(
          2.625 M, 20.841% Params, 2.896 GFLOPs, 24.609% FLOPs, 
          (0): STDCModule(
            0.475 M, 3.770% Params, 0.692 GFLOPs, 5.877% FLOPs, 
            (layers): ModuleList(
              0.472 M, 3.747% Params, 0.688 GFLOPs, 5.844% FLOPs, 
              (0): ConvModule(
                0.066 M, 0.524% Params, 0.272 GFLOPs, 2.308% FLOPs, 
                (conv): Conv2d(0.066 M, 0.520% Params, 0.268 GFLOPs, 2.281% FLOPs, 256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (bn): BatchNorm2d(0.001 M, 0.004% Params, 0.002 GFLOPs, 0.018% FLOPs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.009% FLOPs, inplace=True)
              )
              (1): ConvModule(
                0.295 M, 2.343% Params, 0.302 GFLOPs, 2.570% FLOPs, 
                (conv): Conv2d(0.295 M, 2.341% Params, 0.302 GFLOPs, 2.567% FLOPs, 256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.002% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (2): ConvModule(
                0.074 M, 0.586% Params, 0.076 GFLOPs, 0.643% FLOPs, 
                (conv): Conv2d(0.074 M, 0.585% Params, 0.075 GFLOPs, 0.642% FLOPs, 128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.001% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (3): ConvModule(
                0.037 M, 0.294% Params, 0.038 GFLOPs, 0.322% FLOPs, 
                (conv): Conv2d(0.037 M, 0.293% Params, 0.038 GFLOPs, 0.321% FLOPs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.001% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
            )
            (downsample): ConvModule(
              0.003 M, 0.022% Params, 0.003 GFLOPs, 0.025% FLOPs, 
              (conv): Conv2d(0.002 M, 0.018% Params, 0.002 GFLOPs, 0.020% FLOPs, 256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=256, bias=False)
              (bn): BatchNorm2d(0.001 M, 0.004% Params, 0.001 GFLOPs, 0.004% FLOPs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (skip): AvgPool2d(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.009% FLOPs, kernel_size=3, stride=2, padding=1)
          )
          (1): STDCModule(
            0.538 M, 4.268% Params, 0.551 GFLOPs, 4.683% FLOPs, 
            (layers): ModuleList(
              0.538 M, 4.268% Params, 0.551 GFLOPs, 4.683% FLOPs, 
              (0): ConvModule(
                0.132 M, 1.045% Params, 0.135 GFLOPs, 1.147% FLOPs, 
                (conv): Conv2d(0.131 M, 1.041% Params, 0.134 GFLOPs, 1.141% FLOPs, 512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (bn): BatchNorm2d(0.001 M, 0.004% Params, 0.001 GFLOPs, 0.004% FLOPs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.002% FLOPs, inplace=True)
              )
              (1): ConvModule(
                0.295 M, 2.343% Params, 0.302 GFLOPs, 2.570% FLOPs, 
                (conv): Conv2d(0.295 M, 2.341% Params, 0.302 GFLOPs, 2.567% FLOPs, 256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.002% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (2): ConvModule(
                0.074 M, 0.586% Params, 0.076 GFLOPs, 0.643% FLOPs, 
                (conv): Conv2d(0.074 M, 0.585% Params, 0.075 GFLOPs, 0.642% FLOPs, 128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.001% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (3): ConvModule(
                0.037 M, 0.294% Params, 0.038 GFLOPs, 0.322% FLOPs, 
                (conv): Conv2d(0.037 M, 0.293% Params, 0.038 GFLOPs, 0.321% FLOPs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.001% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
            )
          )
          (2): STDCModule(
            0.538 M, 4.268% Params, 0.551 GFLOPs, 4.683% FLOPs, 
            (layers): ModuleList(
              0.538 M, 4.268% Params, 0.551 GFLOPs, 4.683% FLOPs, 
              (0): ConvModule(
                0.132 M, 1.045% Params, 0.135 GFLOPs, 1.147% FLOPs, 
                (conv): Conv2d(0.131 M, 1.041% Params, 0.134 GFLOPs, 1.141% FLOPs, 512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (bn): BatchNorm2d(0.001 M, 0.004% Params, 0.001 GFLOPs, 0.004% FLOPs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.002% FLOPs, inplace=True)
              )
              (1): ConvModule(
                0.295 M, 2.343% Params, 0.302 GFLOPs, 2.570% FLOPs, 
                (conv): Conv2d(0.295 M, 2.341% Params, 0.302 GFLOPs, 2.567% FLOPs, 256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.002% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (2): ConvModule(
                0.074 M, 0.586% Params, 0.076 GFLOPs, 0.643% FLOPs, 
                (conv): Conv2d(0.074 M, 0.585% Params, 0.075 GFLOPs, 0.642% FLOPs, 128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.001% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (3): ConvModule(
                0.037 M, 0.294% Params, 0.038 GFLOPs, 0.322% FLOPs, 
                (conv): Conv2d(0.037 M, 0.293% Params, 0.038 GFLOPs, 0.321% FLOPs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.001% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
            )
          )
          (3): STDCModule(
            0.538 M, 4.268% Params, 0.551 GFLOPs, 4.683% FLOPs, 
            (layers): ModuleList(
              0.538 M, 4.268% Params, 0.551 GFLOPs, 4.683% FLOPs, 
              (0): ConvModule(
                0.132 M, 1.045% Params, 0.135 GFLOPs, 1.147% FLOPs, 
                (conv): Conv2d(0.131 M, 1.041% Params, 0.134 GFLOPs, 1.141% FLOPs, 512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (bn): BatchNorm2d(0.001 M, 0.004% Params, 0.001 GFLOPs, 0.004% FLOPs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.002% FLOPs, inplace=True)
              )
              (1): ConvModule(
                0.295 M, 2.343% Params, 0.302 GFLOPs, 2.570% FLOPs, 
                (conv): Conv2d(0.295 M, 2.341% Params, 0.302 GFLOPs, 2.567% FLOPs, 256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.002% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (2): ConvModule(
                0.074 M, 0.586% Params, 0.076 GFLOPs, 0.643% FLOPs, 
                (conv): Conv2d(0.074 M, 0.585% Params, 0.075 GFLOPs, 0.642% FLOPs, 128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.001% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (3): ConvModule(
                0.037 M, 0.294% Params, 0.038 GFLOPs, 0.322% FLOPs, 
                (conv): Conv2d(0.037 M, 0.293% Params, 0.038 GFLOPs, 0.321% FLOPs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.001% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
            )
          )
          (4): STDCModule(
            0.538 M, 4.268% Params, 0.551 GFLOPs, 4.683% FLOPs, 
            (layers): ModuleList(
              0.538 M, 4.268% Params, 0.551 GFLOPs, 4.683% FLOPs, 
              (0): ConvModule(
                0.132 M, 1.045% Params, 0.135 GFLOPs, 1.147% FLOPs, 
                (conv): Conv2d(0.131 M, 1.041% Params, 0.134 GFLOPs, 1.141% FLOPs, 512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (bn): BatchNorm2d(0.001 M, 0.004% Params, 0.001 GFLOPs, 0.004% FLOPs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.002% FLOPs, inplace=True)
              )
              (1): ConvModule(
                0.295 M, 2.343% Params, 0.302 GFLOPs, 2.570% FLOPs, 
                (conv): Conv2d(0.295 M, 2.341% Params, 0.302 GFLOPs, 2.567% FLOPs, 256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.002% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (2): ConvModule(
                0.074 M, 0.586% Params, 0.076 GFLOPs, 0.643% FLOPs, 
                (conv): Conv2d(0.074 M, 0.585% Params, 0.075 GFLOPs, 0.642% FLOPs, 128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.001% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (3): ConvModule(
                0.037 M, 0.294% Params, 0.038 GFLOPs, 0.322% FLOPs, 
                (conv): Conv2d(0.037 M, 0.293% Params, 0.038 GFLOPs, 0.321% FLOPs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.001% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
            )
          )
        )
        (4): Sequential(
          6.189 M, 49.127% Params, 1.788 GFLOPs, 15.196% FLOPs, 
          (0): STDCModule(
            1.892 M, 15.018% Params, 0.688 GFLOPs, 5.844% FLOPs, 
            (layers): ModuleList(
              1.886 M, 14.974% Params, 0.686 GFLOPs, 5.827% FLOPs, 
              (0): ConvModule(
                0.263 M, 2.089% Params, 0.27 GFLOPs, 2.295% FLOPs, 
                (conv): Conv2d(0.262 M, 2.081% Params, 0.268 GFLOPs, 2.281% FLOPs, 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (bn): BatchNorm2d(0.001 M, 0.008% Params, 0.001 GFLOPs, 0.009% FLOPs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
              )
              (1): ConvModule(
                1.18 M, 9.369% Params, 0.302 GFLOPs, 2.568% FLOPs, 
                (conv): Conv2d(1.18 M, 9.365% Params, 0.302 GFLOPs, 2.567% FLOPs, 512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.001 M, 0.004% Params, 0.0 GFLOPs, 0.001% FLOPs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (2): ConvModule(
                0.295 M, 2.343% Params, 0.076 GFLOPs, 0.642% FLOPs, 
                (conv): Conv2d(0.295 M, 2.341% Params, 0.075 GFLOPs, 0.642% FLOPs, 256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.001% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, inplace=True)
              )
              (3): ConvModule(
                0.148 M, 1.173% Params, 0.038 GFLOPs, 0.322% FLOPs, 
                (conv): Conv2d(0.147 M, 1.171% Params, 0.038 GFLOPs, 0.321% FLOPs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.001% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, inplace=True)
              )
            )
            (downsample): ConvModule(
              0.006 M, 0.045% Params, 0.001 GFLOPs, 0.012% FLOPs, 
              (conv): Conv2d(0.005 M, 0.037% Params, 0.001 GFLOPs, 0.010% FLOPs, 512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=512, bias=False)
              (bn): BatchNorm2d(0.001 M, 0.008% Params, 0.0 GFLOPs, 0.002% FLOPs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (skip): AvgPool2d(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, kernel_size=3, stride=2, padding=1)
          )
          (1): STDCModule(
            2.148 M, 17.055% Params, 0.55 GFLOPs, 4.676% FLOPs, 
            (layers): ModuleList(
              2.148 M, 17.055% Params, 0.55 GFLOPs, 4.676% FLOPs, 
              (0): ConvModule(
                0.525 M, 4.170% Params, 0.135 GFLOPs, 1.144% FLOPs, 
                (conv): Conv2d(0.524 M, 4.162% Params, 0.134 GFLOPs, 1.141% FLOPs, 1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (bn): BatchNorm2d(0.001 M, 0.008% Params, 0.0 GFLOPs, 0.002% FLOPs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (1): ConvModule(
                1.18 M, 9.369% Params, 0.302 GFLOPs, 2.568% FLOPs, 
                (conv): Conv2d(1.18 M, 9.365% Params, 0.302 GFLOPs, 2.567% FLOPs, 512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.001 M, 0.004% Params, 0.0 GFLOPs, 0.001% FLOPs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (2): ConvModule(
                0.295 M, 2.343% Params, 0.076 GFLOPs, 0.642% FLOPs, 
                (conv): Conv2d(0.295 M, 2.341% Params, 0.075 GFLOPs, 0.642% FLOPs, 256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.001% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, inplace=True)
              )
              (3): ConvModule(
                0.148 M, 1.173% Params, 0.038 GFLOPs, 0.322% FLOPs, 
                (conv): Conv2d(0.147 M, 1.171% Params, 0.038 GFLOPs, 0.321% FLOPs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.001% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, inplace=True)
              )
            )
          )
          (2): STDCModule(
            2.148 M, 17.055% Params, 0.55 GFLOPs, 4.676% FLOPs, 
            (layers): ModuleList(
              2.148 M, 17.055% Params, 0.55 GFLOPs, 4.676% FLOPs, 
              (0): ConvModule(
                0.525 M, 4.170% Params, 0.135 GFLOPs, 1.144% FLOPs, 
                (conv): Conv2d(0.524 M, 4.162% Params, 0.134 GFLOPs, 1.141% FLOPs, 1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (bn): BatchNorm2d(0.001 M, 0.008% Params, 0.0 GFLOPs, 0.002% FLOPs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (1): ConvModule(
                1.18 M, 9.369% Params, 0.302 GFLOPs, 2.568% FLOPs, 
                (conv): Conv2d(1.18 M, 9.365% Params, 0.302 GFLOPs, 2.567% FLOPs, 512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.001 M, 0.004% Params, 0.0 GFLOPs, 0.001% FLOPs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
              )
              (2): ConvModule(
                0.295 M, 2.343% Params, 0.076 GFLOPs, 0.642% FLOPs, 
                (conv): Conv2d(0.295 M, 2.341% Params, 0.075 GFLOPs, 0.642% FLOPs, 256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.001% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, inplace=True)
              )
              (3): ConvModule(
                0.148 M, 1.173% Params, 0.038 GFLOPs, 0.322% FLOPs, 
                (conv): Conv2d(0.147 M, 1.171% Params, 0.038 GFLOPs, 0.321% FLOPs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.001% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, inplace=True)
              )
            )
          )
        )
      )
    )
    init_cfg={'type': 'Pretrained', 'checkpoint': './pretrained/stdc2_in1k-pre_512x1024_80k_cityscapes_20220224_073048-1f8f0f6c.pth'}
    (arms): ModuleList(
      1.803 M, 14.315% Params, 0.907 GFLOPs, 7.705% FLOPs, 
      (0): AttentionRefinementModule(
        1.197 M, 9.499% Params, 0.302 GFLOPs, 2.568% FLOPs, 
        (conv_layer): ConvModule(
          1.18 M, 9.367% Params, 0.302 GFLOPs, 2.567% FLOPs, 
          (conv): Conv2d(1.18 M, 9.365% Params, 0.302 GFLOPs, 2.567% FLOPs, 1024, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.001% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, inplace=True)
        )
        (atten_conv_layer): Sequential(
          0.017 M, 0.132% Params, 0.0 GFLOPs, 0.000% FLOPs, 
          (0): AdaptiveAvgPool2d(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, output_size=(1, 1))
          (1): ConvModule(
            0.017 M, 0.132% Params, 0.0 GFLOPs, 0.000% FLOPs, 
            (conv): Conv2d(0.016 M, 0.130% Params, 0.0 GFLOPs, 0.000% FLOPs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.000% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
          (2): Sigmoid(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, )
        )
      )
      (1): AttentionRefinementModule(
        0.607 M, 4.816% Params, 0.605 GFLOPs, 5.138% FLOPs, 
        (conv_layer): ConvModule(
          0.59 M, 4.684% Params, 0.604 GFLOPs, 5.136% FLOPs, 
          (conv): Conv2d(0.59 M, 4.682% Params, 0.604 GFLOPs, 5.133% FLOPs, 512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.002% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
        )
        (atten_conv_layer): Sequential(
          0.017 M, 0.132% Params, 0.0 GFLOPs, 0.001% FLOPs, 
          (0): AdaptiveAvgPool2d(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, output_size=(1, 1))
          (1): ConvModule(
            0.017 M, 0.132% Params, 0.0 GFLOPs, 0.000% FLOPs, 
            (conv): Conv2d(0.016 M, 0.130% Params, 0.0 GFLOPs, 0.000% FLOPs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.000% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
          (2): Sigmoid(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, )
        )
      )
    )
    (convs): ModuleList(
      0.295 M, 2.345% Params, 0.757 GFLOPs, 6.433% FLOPs, 
      (0): ConvModule(
        0.148 M, 1.173% Params, 0.151 GFLOPs, 1.287% FLOPs, 
        (conv): Conv2d(0.147 M, 1.171% Params, 0.151 GFLOPs, 1.283% FLOPs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.002% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
      )
      (1): ConvModule(
        0.148 M, 1.173% Params, 0.606 GFLOPs, 5.146% FLOPs, 
        (conv): Conv2d(0.147 M, 1.171% Params, 0.604 GFLOPs, 5.133% FLOPs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.001 GFLOPs, 0.009% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (activate): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
      )
    )
    (conv_avg): ConvModule(
      0.131 M, 1.043% Params, 0.0 GFLOPs, 0.001% FLOPs, 
      (conv): Conv2d(0.131 M, 1.041% Params, 0.0 GFLOPs, 0.001% FLOPs, 1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.0 GFLOPs, 0.000% FLOPs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, inplace=True)
    )
    (ffm): FeatureFusionModule(
      0.132 M, 1.045% Params, 0.407 GFLOPs, 3.458% FLOPs, 
      (conv0): ConvModule(
        0.099 M, 0.784% Params, 0.406 GFLOPs, 3.449% FLOPs, 
        (conv): Conv2d(0.098 M, 0.780% Params, 0.403 GFLOPs, 3.422% FLOPs, 384, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(0.001 M, 0.004% Params, 0.002 GFLOPs, 0.018% FLOPs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (activate): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.009% FLOPs, inplace=True)
      )
      (attention): Sequential(
        0.033 M, 0.260% Params, 0.001 GFLOPs, 0.009% FLOPs, 
        (0): AdaptiveAvgPool2d(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.009% FLOPs, output_size=(1, 1))
        (1): ConvModule(
          0.016 M, 0.130% Params, 0.0 GFLOPs, 0.000% FLOPs, 
          (conv): Conv2d(0.016 M, 0.130% Params, 0.0 GFLOPs, 0.000% FLOPs, 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, inplace=True)
        )
        (2): ConvModule(
          0.016 M, 0.130% Params, 0.0 GFLOPs, 0.000% FLOPs, 
          (conv): Conv2d(0.016 M, 0.130% Params, 0.0 GFLOPs, 0.000% FLOPs, 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (3): Sigmoid(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, )
      )
    )
  )
  (decode_head): FCNHead(
    0.591 M, 4.690% Params, 2.421 GFLOPs, 20.577% FLOPs, input_transform=None, ignore_index=255, align_corners=False
    (loss_decode): CrossEntropyLoss(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, avg_non_ignore=False)
    (conv_seg): Conv2d(0.001 M, 0.004% Params, 0.002 GFLOPs, 0.018% FLOPs, 256, 2, kernel_size=(1, 1), stride=(1, 1))
    (dropout): Dropout2d(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, p=0.1, inplace=False)
    (convs): Sequential(
      0.59 M, 4.686% Params, 2.419 GFLOPs, 20.559% FLOPs, 
      (0): ConvModule(
        0.59 M, 4.686% Params, 2.419 GFLOPs, 20.559% FLOPs, 
        (conv): Conv2d(0.59 M, 4.682% Params, 2.416 GFLOPs, 20.532% FLOPs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(0.001 M, 0.004% Params, 0.002 GFLOPs, 0.018% FLOPs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (activate): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.009% FLOPs, inplace=True)
      )
    )
  )
  init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}
  (auxiliary_head): ModuleList(
    0.296 M, 2.347% Params, 0.0 GFLOPs, 0.000% FLOPs, 
    (0): FCNHead(
      0.074 M, 0.587% Params, 0.0 GFLOPs, 0.000% FLOPs, input_transform=None, ignore_index=255, align_corners=False
      (loss_decode): CrossEntropyLoss(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, avg_non_ignore=False)
      (conv_seg): Conv2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.000% FLOPs, 64, 2, kernel_size=(1, 1), stride=(1, 1))
      (dropout): Dropout2d(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, p=0.1, inplace=False)
      (convs): Sequential(
        0.074 M, 0.586% Params, 0.0 GFLOPs, 0.000% FLOPs, 
        (0): ConvModule(
          0.074 M, 0.586% Params, 0.0 GFLOPs, 0.000% FLOPs, 
          (conv): Conv2d(0.074 M, 0.585% Params, 0.0 GFLOPs, 0.000% FLOPs, 128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.000% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, inplace=True)
        )
      )
    )
    init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}
    (1): FCNHead(
      0.074 M, 0.587% Params, 0.0 GFLOPs, 0.000% FLOPs, input_transform=None, ignore_index=255, align_corners=False
      (loss_decode): CrossEntropyLoss(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, avg_non_ignore=False)
      (conv_seg): Conv2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.000% FLOPs, 64, 2, kernel_size=(1, 1), stride=(1, 1))
      (dropout): Dropout2d(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, p=0.1, inplace=False)
      (convs): Sequential(
        0.074 M, 0.586% Params, 0.0 GFLOPs, 0.000% FLOPs, 
        (0): ConvModule(
          0.074 M, 0.586% Params, 0.0 GFLOPs, 0.000% FLOPs, 
          (conv): Conv2d(0.074 M, 0.585% Params, 0.0 GFLOPs, 0.000% FLOPs, 128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.000% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, inplace=True)
        )
      )
    )
    init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}
    (2): STDCHead(
      0.148 M, 1.173% Params, 0.0 GFLOPs, 0.000% FLOPs, input_transform=None, ignore_index=255, align_corners=False
      (loss_decode): ModuleList(
        0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, 
        (0): CrossEntropyLoss(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, avg_non_ignore=False)
        (1): DiceLoss(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, )
      )
      (conv_seg): Conv2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.000% FLOPs, 64, 2, kernel_size=(1, 1), stride=(1, 1))
      (dropout): Dropout2d(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, p=0.1, inplace=False)
      (convs): Sequential(
        0.148 M, 1.172% Params, 0.0 GFLOPs, 0.000% FLOPs, 
        (0): ConvModule(
          0.148 M, 1.172% Params, 0.0 GFLOPs, 0.000% FLOPs, 
          (conv): Conv2d(0.147 M, 1.171% Params, 0.0 GFLOPs, 0.000% FLOPs, 256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.0 GFLOPs, 0.000% FLOPs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (activate): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, inplace=True)
        )
      )
    )
    init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}
  )
)
==============================
Input shape: (3, 512, 512)
Flops: 11.77 GFLOPs
Params: 12.6 M
==============================
!!!Please be cautious if you use the results in papers. You may need to check if all ops are supported and verify that the flops computation is correct.
