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ReUpNet(
  (hidden): Sequential(
    (0): DenseNetBlock(
      (layers): ModuleList(
        (0): Sequential(
          (0): Conv1d(3, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): Sequential(
          (0): Conv1d(40, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (2): Sequential(
          (0): Conv1d(80, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (3): Sequential(
          (0): Conv1d(120, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (4): Sequential(
          (0): Conv1d(160, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
    )
    (1): DenseNetBlock(
      (layers): ModuleList(
        (0): Sequential(
          (0): Conv1d(40, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): Sequential(
          (0): Conv1d(40, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (2): Sequential(
          (0): Conv1d(80, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (3): Sequential(
          (0): Conv1d(120, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (4): Sequential(
          (0): Conv1d(160, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
    )
    (2): DenseNetBlock(
      (layers): ModuleList(
        (0): Sequential(
          (0): Conv1d(40, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): Sequential(
          (0): Conv1d(40, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (2): Sequential(
          (0): Conv1d(80, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (3): Sequential(
          (0): Conv1d(120, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (4): Sequential(
          (0): Conv1d(160, 40, kernel_size=(9,), stride=(1,), padding=(4,))
          (1): ReLU()
          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
    )
    (3): Conv1d(40, 8, kernel_size=(9,), stride=(1,), padding=(4,))
    (4): Sigmoid()
  )
  (subpix): Subpixel1d()
)
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