AssertionError: EncoderDecoderLandcover: both backbone and segmentor set pretrained weight

[wangchong666]

Error occur after running https://github.com/aiearth-damo/deeplearning/blob/main/quickstart.ipynb

# 训练
trainer.train(validate=False)

2024-04-29 15:54:42,338 - mmseg - INFO - Multi-processing start method is `None`
2024-04-29 15:54:42,338 - mmseg - INFO - Multi-processing start method is `None`
2024-04-29 15:54:42,341 - mmseg - INFO - OpenCV num_threads is `24
2024-04-29 15:54:42,341 - mmseg - INFO - OpenCV num_threads is `24
2024-04-29 15:54:42,395 - root - INFO - Loaded 16800 images
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
File ~/miniconda3/envs/py310/lib/python3.10/site-packages/mmcv/utils/registry.py:69, in build_from_cfg(cfg, registry, default_args)
     68 try:
---> 69     return obj_cls(**args)
     70 except Exception as e:
     71     # Normal TypeError does not print class name.

File ~/deeplearning/aiearth/deeplearning/models/landcover/segmentors/encoder_decoder_landcover.py:15, in EncoderDecoderLandcover.__init__(self, **kwargs)
     14 def __init__(self, **kwargs):
---> 15     super(EncoderDecoderLandcover, self).__init__(**kwargs)

File ~/miniconda3/envs/py310/lib/python3.10/site-packages/mmseg/models/segmentors/encoder_decoder.py:33, in EncoderDecoder.__init__(self, backbone, decode_head, neck, auxiliary_head, train_cfg, test_cfg, pretrained, init_cfg)
     32 if pretrained is not None:
---> 33     assert backbone.get('pretrained') is None, \
     34         'both backbone and segmentor set pretrained weight'
     35     backbone.pretrained = pretrained

AssertionError: both backbone and segmentor set pretrained weight

During handling of the above exception, another exception occurred:

AssertionError                            Traceback (most recent call last)
Cell In[7], line 2
      1 # 训练
----> 2 trainer.train(validate=False)

File ~/deeplearning/aiearth/deeplearning/trainer/mmseg/mmseg_trainer.py:268, in MMSegTrainer.train(self, validate, distributed)
    266 datasets = self.build_datasets()
    267 # Build the detector
--> 268 model = self.build_model()
    269 # Add an attribute for visualization convenience
    270 model.CLASSES = datasets[0].CLASSES

File ~/deeplearning/aiearth/deeplearning/trainer/mmseg/landcover_trainer.py:43, in LandcoverTrainer.build_model(self)
     40 algorithm = self.cfg.get("algorithm", None)
     42 if algorithm is None:
---> 43     model = build_segmentor(
     44         self.cfg.model,
     45         train_cfg=self.cfg.get("train_cfg"),
     46         test_cfg=self.cfg.get("test_cfg"),
     47     )
     48 else:
     49     # Different from mmsegmentation
     50     # replace `model` to `algorithm`
     51     if self.cfg.algorithm.architecture.model == "same":

File ~/miniconda3/envs/py310/lib/python3.10/site-packages/mmseg/models/builder.py:48, in build_segmentor(cfg, train_cfg, test_cfg)
     44 assert cfg.get('train_cfg') is None or train_cfg is None, \
     45     'train_cfg specified in both outer field and model field '
     46 assert cfg.get('test_cfg') is None or test_cfg is None, \
     47     'test_cfg specified in both outer field and model field '
---> 48 return SEGMENTORS.build(
     49     cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))

File ~/miniconda3/envs/py310/lib/python3.10/site-packages/mmcv/utils/registry.py:237, in Registry.build(self, *args, **kwargs)
    236 def build(self, *args, **kwargs):
--> 237     return self.build_func(*args, **kwargs, registry=self)

File ~/miniconda3/envs/py310/lib/python3.10/site-packages/mmcv/cnn/builder.py:27, in build_model_from_cfg(cfg, registry, default_args)
     25     return Sequential(*modules)
     26 else:
---> 27     return build_from_cfg(cfg, registry, default_args)

File ~/miniconda3/envs/py310/lib/python3.10/site-packages/mmcv/utils/registry.py:72, in build_from_cfg(cfg, registry, default_args)
     69     return obj_cls(**args)
     70 except Exception as e:
     71     # Normal TypeError does not print class name.
---> 72     raise type(e)(f'{obj_cls.__name__}: {e}')

AssertionError: EncoderDecoderLandcover: both backbone and segmentor set pretrained weight