CoordRegressionNetwork(
  (resnet): Sequential(
    (0): Sequential(
      (0): 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): ReLU6(inplace=True)
      )
      (1): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (2): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
          (4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (3): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
          (4): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (4): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)
          (4): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (5): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
          (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (6): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
          (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (7): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)
          (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (8): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
          (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (9): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
          (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (10): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
          (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (11): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
          (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (12): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
          (4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (13): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
          (4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (14): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)
          (4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (15): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
          (4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (16): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
          (4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (17): InvertedResidual(
        (conv): Sequential(
          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
          (3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
          (4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU6(inplace=True)
          (6): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (7): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (18): Sequential(
        (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU6(inplace=True)
      )
    )
    (1): Conv2d(1280, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (2): DUC(
      (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (pixel_shuffle): PixelShuffle(upscale_factor=2)
    )
    (3): DUC(
      (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (pixel_shuffle): PixelShuffle(upscale_factor=2)
    )
    (4): DUC(
      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (pixel_shuffle): PixelShuffle(upscale_factor=2)
    )
  )
  (hm_conv): Conv2d(32, 9, kernel_size=(1, 1), stride=(1, 1), bias=False)
)