How to use a pre-trained deep learning model in transform with cuda ?

[think-ya]

My code is as follow.

@TRANSFORMS.register_module(force=True)
class LoadDefectDataFromFileForFixedSize(LoadImageFromFile):
    def __init__(self, train=True, 
                 **kwargs) -> None:
        super().__init__(**kwargs)
        self.inpaint = LaMa('cuda' if torch.cuda.is_available() else 'cpu') 

LaMa is used to initialize a pre-trained model.

When I run it, I get the following error message.

Traceback (most recent call last):
  File "/root/miniconda3/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/root/miniconda3/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/root/.vscode-server/extensions/ms-python.python-2023.6.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module>
    cli.main()
  File "/root/.vscode-server/extensions/ms-python.python-2023.6.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main
    run()
  File "/root/.vscode-server/extensions/ms-python.python-2023.6.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file
    runpy.run_path(target, run_name="__main__")
  File "/root/.vscode-server/extensions/ms-python.python-2023.6.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "/root/.vscode-server/extensions/ms-python.python-2023.6.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "/root/.vscode-server/extensions/ms-python.python-2023.6.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code
    exec(code, run_globals)
  File "./tools/train.py", line 162, in <module>
    main()
  File "./tools/train.py", line 155, in main
    runner.train()
  File "/root/miniconda3/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1706, in train
    model = self.train_loop.run()  # type: ignore
  File "/root/miniconda3/lib/python3.8/site-packages/mmengine/runner/loops.py", line 96, in run
    self.run_epoch()
  File "/root/miniconda3/lib/python3.8/site-packages/mmengine/runner/loops.py", line 111, in run_epoch
    for idx, data_batch in enumerate(self.dataloader):
  File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 628, in __next__
    data = self._next_data()
  File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1333, in _next_data
    return self._process_data(data)
  File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1359, in _process_data
    data.reraise()
  File "/root/miniconda3/lib/python3.8/site-packages/torch/_utils.py", line 543, in reraise
    raise exception
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 302, in _worker_loop
    data = fetcher.fetch(index)
  File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 58, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 58, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/root/miniconda3/lib/python3.8/site-packages/mmengine/dataset/base_dataset.py", line 413, in __getitem__
    data = self.prepare_data(idx)
  File "/root/miniconda3/lib/python3.8/site-packages/mmengine/dataset/base_dataset.py", line 797, in prepare_data
    return self.pipeline(data_info)
  File "/root/miniconda3/lib/python3.8/site-packages/mmengine/dataset/base_dataset.py", line 59, in __call__
    data = t(data)
  File "/root/miniconda3/lib/python3.8/site-packages/mmcv/transforms/base.py", line 12, in __call__
    return self.transform(results)
  File "/root/ys/den_next/./src/transforms/load_defect.py", line 104, in transform
    img_inpainting = self.inpaint(img[:, :, ::-1], other_defect_mask)
  File "/root/miniconda3/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/root/ys/den_next/./src/transforms/inpaint/lama.py", line 85, in __call__
    inpaint_result = self._pad_forward(image, mask)
  File "/root/ys/den_next/./src/transforms/inpaint/lama.py", line 68, in _pad_forward
    result = self.forward(pad_image, pad_mask)
  File "/root/ys/den_next/./src/transforms/inpaint/lama.py", line 49, in forward
    image = torch.from_numpy(image).unsqueeze(0).to(self.device)
  File "/root/miniconda3/lib/python3.8/site-packages/torch/cuda/__init__.py", line 217, in _lazy_init
    raise RuntimeError(
RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method

[think-ya]

It should be implemented in 'Data Preprocessor'.
In the doc of mmpretain, shows:
The data preprocessor is also a component to process the data before feeding data to the neural network. Comparing with the data transforms, the data preprocessor is a module of the classifier, and it takes a batch of data to process, which means it can use GPU and batch to accelebrate the processing.
https://mmpretrain.readthedocs.io/en/latest/api/data_process.html#data-preprocessors