If I try to recreate your model with plain PyTorch modules, I get the expected parameters from layer1, 2, and 5:
model = nn.Sequential(
    nn.Conv1d(1, 1, 1),
    nn.LeakyReLU(),
    nn.BatchNorm1d(1),
    nn.MaxPool1d(2),
    nn.MaxUnpool1d(2),
    nn.ConvTranspose1d(1, 1, 1))

dict(model.named_parameters())
> {'0.weight': Parameter containing:
 tensor([[[0.1611]]], requires_grad=True),
 '0.bias': Parameter containing:
 tensor([-0.0869], requires_grad=True),
 '2.weight': Parameter containing:
 tensor([1.], requires_grad=True),
 '2.bias': Parameter containing:
 tensor([0.], requires_grad=True),
 '5.weight': Parameter containing:
 tensor([[[-0.5314]]], requires_grad=True),
 '5.bias': Parameter containing:
 tensor([0.6917], requires_grad=True)}

so I assume your custom implementations might use additional parameters.