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+1. Would be really helpful to share an inference demo for SETR. I tried using SETR-Naive model (replaced the given PSPNet model in the demo) but end up with the following error (with trace).
File~/projects/video-segcap/lib/SETR/mmseg/datasets/pipelines/test_time_aug.py:59, inMultiScaleFlipAug.__init__(self, transforms, img_scale, img_ratios, flip, flip_direction)
53def__init__(self,
54transforms,
55img_scale,
56img_ratios=None,
57flip=False,
58flip_direction='horizontal'):
--->59self.transforms=Compose(transforms)
60ifimg_ratiosisnotNone:
61# mode 1: given a scale and a range of image ratio62img_ratios=img_ratiosifisinstance(img_ratios,
63list) else [img_ratios]
File~/projects/video-segcap/lib/SETR/mmseg/datasets/pipelines/compose.py:22, inCompose.__init__(self, transforms)
20fortransformintransforms:
21ifisinstance(transform, dict):
--->22transform=build_from_cfg(transform, PIPELINES)
23self.transforms.append(transform)
24elifcallable(transform):
File~/install/miniconda3/envs/clip/lib/python3.8/site-packages/mmcv/utils/registry.py:171, inbuild_from_cfg(cfg, registry, default_args)
167else:
168raiseTypeError(
169f'type must be a str or valid type, but got {type(obj_type)}')
-->171returnobj_cls(**args)
TypeError: __init__() gotanunexpectedkeywordargument'dataset'
The demo in your repo is written for pspnet, so could you share the demo for SETR?
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