hrnet

<class 'list'>: [HighResolutionModule(
  (branches): ModuleList(
    (0): Sequential(
      (0): BasicBlock(
        (conv1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
        (relu): ReLU()
        (conv2): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
      )
      (1): BasicBlock(
        (conv1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
        (relu): ReLU()
        (conv2): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
      )
      (2): BasicBlock(
        (conv1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
        (relu): ReLU()
        (conv2): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
      )
      (3): BasicBlock(
        (conv1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
        (relu): ReLU()
        (conv2): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
    (1): Sequential(
      (0): BasicBlock(
        (conv1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
        (relu): ReLU()
        (conv2): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(96, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
      )
      (1): BasicBlock(
        (conv1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
        (relu): ReLU()
        (conv2): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(96, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
      )
      (2): BasicBlock(
        (conv1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
        (relu): ReLU()
        (conv2): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(96, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
      )
      (3): BasicBlock(
        (conv1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
        (relu): ReLU()
        (conv2): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(96, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (fuse_layers): ModuleList(
    (0): ModuleList(
      (0): None
      (1): Sequential(
        (0): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(48, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
    (1): ModuleList(
      (0): Sequential(
        (0): Sequential(
          (0): Conv2d(48, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (1): BatchNorm2d(96, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
        )
      )
      (1): None
    )
  )
  (relu): ReLU()
)]