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

Branch

main branch (1.x version, such as v1.0.0, or dev-1.x branch)

Prerequisite

[X] I have searched Issues and Discussions but cannot get the expected help.
[X] I have read the documentation but cannot get the expected help.
[X] The bug has not been fixed in the latest version.

Environment

sys.platform: linux Python: 3.9.18 | packaged by conda-forge | (main, Aug 30 2023, 03:49:32) [GCC 12.3.0] CUDA available: True numpy_random_seed: 2147483648 GPU 0,1,2,3,4,5,6,7: Tesla V100-SXM2-32GB CUDA_HOME: /usr/local/cuda-11.7 NVCC: Cuda compilation tools, release 11.7, V11.7.64 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 2.0.1+cu117 PyTorch compiling details: PyTorch built with:

GCC 9.3
C++ Version: 201703
Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
OpenMP 201511 (a.k.a. OpenMP 4.5)
LAPACK is enabled (usually provided by MKL)
NNPACK is enabled
CPU capability usage: AVX2
CUDA Runtime 11.7
NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
CuDNN 8.7 (built against CUDA 11.8)
- Built with CuDNN 8.5
Magma 2.6.1
Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.15.2+cu117 OpenCV: 4.8.1 MMEngine: 0.9.0 MMAction2: 1.2.0+4d6c934 MMCV: 2.1.0

Describe the bug

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.

Reproduces the problem - code sample

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Reproduces the problem - command or script

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Reproduces the problem - error message

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Additional information

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open-mmlab / mmaction2