How to change dataset settings and add optimizer to parameters other than model parameters when using Runner?

[haochuan-li]

Hi!
I wonder is it possible to change the dataset settings and add optimizer to parameters other than model parameters when using Runner. For example,

# My Custom Dataset
def CustomDataset(BaseDataset):
    def load_data_list(self):
         ...
         return [dict(inputs=torch.rand(1,3,224,224).numpy(), gt_label = torch.rand(1).numpy() for i in range(bs)]

# Configs for Runner -> cfg
train_pipeline_cfg = [dict(type=...)]
ds = dict(type='CustomDataset',pipeline=train_pipeline_cfg)
train_dataloader_cfg = dict(
                    batch_size=32,
                    num_workers=8,
                    persistent_workers=True,
                    sampler=dict(type='DefaultSampler', shuffle=True),
                    dataset=ds)
...
runner = Runner.from_cfg(cfg)

# After created runner, here I want to initialize an optimizer for custom dataset and want to set requires_grad -> True in the custom dataset

# Set the require_grad of data in Custom Dataset to True
(????) = (????).requires_grad_(True) # What should I put in the (????) 
optimizer_data = torch.optim.SGD([????]) # What should I put in the optimizer input to get the grad in Custom Dataset? 

runner.train()

Can anyone enlighten me on this?
Thanks.