How to apply this model to large-sized images? about open-cd HOT 2 OPEN

Thanks for your attention, you can refer here.

from open-cd.

Thanks for your attention, you can refer here.

Thank you so much for your prompt reply. I would like to provide more specific details regarding my question. When I modify the line from 'test_cfg=dict(mode='whole')' to 'test_cfg=dict(mode='slide', crop_size=crop_size, stride=(crop_size[0]//2, crop_size[1]//2))', I would like to know how the remaining test configuration parameters should be adjusted accordingly.

Because I intend to switch the test dataset to two large-sized images,without labels, what changes should I make to the test_dataset parameters? The origin test_dataset in config is:

test_dataloader = dict(
batch_size=1,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='LEVIR_CD_Dataset',
data_root='data/LEVIR-CD',
data_prefix=dict(
seg_map_path='test/label',
img_path_from='test/A',
img_path_to='test/B'),
pipeline=[
dict(type='MultiImgLoadImageFromFile'),
dict(type='MultiImgResize', scale=(1024, 1024), keep_ratio=True),
dict(type='MultiImgLoadAnnotations'),
dict(type='MultiImgPackSegInputs')
]))

Should I modify the dataset type（'LEVIR_CD_Dataset'） or create a customized dataset?

Additionally, could you please advise on any other parameters that are relevant to the test section and may require modification?

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