/home/keli/anaconda3/envs/mmrazor/lib/python3.8/site-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  /opt/conda/conda-bld/pytorch_1659484810403/work/aten/src/ATen/native/TensorShape.cpp:2894.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Traceback (most recent call last):
  File "/home/keli/anaconda3/envs/mmrazor/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg
    obj = obj_cls(**args)  # type: ignore
  File "/home/keli/Nuonepeaceyy/MMLAB/mmrazor/mmrazor/models/algorithms/pruning/dcff.py", line 62, in __init__
    super().__init__(architecture, mutator_cfg, data_preprocessor,
  File "/home/keli/Nuonepeaceyy/MMLAB/mmrazor/mmrazor/models/algorithms/pruning/ite_prune_algorithm.py", line 137, in __init__
    self.mutator.prepare_from_supernet(self.architecture)
  File "/home/keli/Nuonepeaceyy/MMLAB/mmrazor/mmrazor/models/mutators/channel_mutator/channel_mutator.py", line 104, in prepare_from_supernet
    units = self._prepare_from_tracer(supernet, self.parse_cfg)
  File "/home/keli/Nuonepeaceyy/MMLAB/mmrazor/mmrazor/models/mutators/channel_mutator/channel_mutator.py", line 300, in _prepare_from_tracer
    unit_configs = tracer.analyze(model)
  File "/home/keli/Nuonepeaceyy/MMLAB/mmrazor/mmrazor/models/task_modules/tracer/channel_analyzer.py", line 127, in analyze
    return self._find_mutable_units(model, unit_configs)
  File "/home/keli/Nuonepeaceyy/MMLAB/mmrazor/mmrazor/models/task_modules/tracer/channel_analyzer.py", line 154, in _find_mutable_units
    template_output = model(**inputs)
  File "/home/keli/anaconda3/envs/mmrazor/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/keli/.local/lib/python3.8/site-packages/mmdet/models/detectors/base.py", line 92, in forward
    return self.loss(inputs, data_samples)
  File "/home/keli/.local/lib/python3.8/site-packages/mmdet/models/detectors/single_stage.py", line 78, in loss
    losses = self.bbox_head.loss(x, batch_data_samples)
  File "/home/keli/Nuonepeaceyy/MMLAB/mmyolo/mmyolo/models/dense_heads/yolov5_head.py", line 459, in loss
    losses = super().loss(x, batch_data_samples)
  File "/home/keli/.local/lib/python3.8/site-packages/mmdet/models/dense_heads/base_dense_head.py", line 123, in loss
    losses = self.loss_by_feat(*loss_inputs)
  File "/home/keli/Nuonepeaceyy/MMLAB/mmyolo/mmyolo/models/dense_heads/yolov8_head.py", line 314, in loss_by_feat
    gt_info = self.gt_instances_preprocess(batch_gt_instances, num_imgs)
  File "/home/keli/Nuonepeaceyy/MMLAB/mmyolo/mmyolo/models/dense_heads/yolov8_head.py", line 424, in gt_instances_preprocess
    bboxes = gt_instance.bboxes
AttributeError: 'dict' object has no attribute 'bboxes'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "tools/pruning/get_channel_units.py", line 84, in <module>
    main()
  File "tools/pruning/get_channel_units.py", line 48, in main
    model = MODELS.build(config['model'])
  File "/home/keli/anaconda3/envs/mmrazor/lib/python3.8/site-packages/mmengine/registry/registry.py", line 521, in build
    return self.build_func(cfg, *args, **kwargs, registry=self)
  File "/home/keli/anaconda3/envs/mmrazor/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 240, in build_model_from_cfg
    return build_from_cfg(cfg, registry, default_args)
  File "/home/keli/anaconda3/envs/mmrazor/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 135, in build_from_cfg
    raise type(e)(
AttributeError: class `DCFF` in mmrazor/models/algorithms/pruning/dcff.py: 'dict' object has no attribute 'bboxes'

_base_ = 'mmyolo::yolov8/yolov8_l_syncbn_fast_8xb16-500e_coco.py'

data_root = 'data/coco/'

architecture = _base_.model

model = dict(
    _delete_=True,
    _scope_='mmrazor',
    type='DCFF',
    architecture=architecture,
    mutator_cfg=dict(
        type='DCFFChannelMutator',
        channel_unit_cfg=dict(
            type='DCFFChannelUnit', default_args=dict(choice_mode='ratio')),
        parse_cfg=dict(
            type='ChannelAnalyzer',
            demo_input=(1, 3, 224, 224),
            tracer_type='FxTracer')),
    # target_pruning_ratio=target_pruning_ratio,
    step_freq=1,
    linear_schedule=False)

# del _base_.train_dataloader.collate_fn
del _base_.custom_hooks

train_dataloader = dict(
    dataset=dict(
        _delete_=True,
        type='mmyolo.YOLOv5CocoDataset',
        data_root=data_root,
        # metainfo=_base_.metainfo,
        data_prefix=dict(img='train/'),
        ann_file='annotations/voc07_train.json',
        filter_cfg=dict(filter_empty_gt=False, min_size=32),
        pipeline=_base_.train_pipeline))
val_dataloader = dict(
    dataset=dict(
        data_root=data_root,
        # metainfo=_base_.metainfo,
        data_prefix=dict(img='val/'),
        ann_file='annotations/voc07_val.json'))
test_dataloader = dict(
    dataset=dict(
        data_root=data_root,
        # metainfo=_base_.metainfo,
        data_prefix=dict(img='test/'),
        ann_file='annotations/voc07_test.json'))
val_evaluator = dict(ann_file=data_root + 'annotations/voc07_val.json')
test_evaluator = dict(ann_file=data_root + 'annotations/voc07_test.json')

model_wrapper = dict(
    type='mmcv.MMDistributedDataParallel', find_unused_parameters=True)

default_hooks = dict(param_scheduler=dict(max_epochs=150))
train_cfg = dict(max_epochs=150, val_interval=5)
val_cfg = dict(_delete_=True, type='mmrazor.ItePruneValLoop')