algorithm = dict(
    type='GeneralDistill',
    architecture=dict(
        type='MMDetArchitecture',
        model=dict(
            type='mmdet.YOLOX',
            input_size=(640, 640),
            random_size_range=(15, 25),
            random_size_interval=10,
            backbone=dict(
                type='ResNet',
                depth=18,
                num_stages=4,
                out_indices=(1, 2, 3),
                norm_cfg=dict(type='BN', requires_grad=True),
                norm_eval=True,
                style='pytorch',
                init_cfg=dict(
                    type='Pretrained', checkpoint='torchvision://resnet18')),
            neck=dict(
                type='YOLOXPAFPN',
                in_channels=[128, 256, 512],
                out_channels=128,
                num_csp_blocks=1),
            bbox_head=dict(
                type='YOLOXHead',
                num_classes=1,
                in_channels=128,
                feat_channels=128),
            train_cfg=dict(
                assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
            test_cfg=dict(
                score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))),
    with_student_loss=True,
    with_teacher_loss=False,
    distiller=dict(
        type='SingleTeacherDistiller',
        teacher=dict(
            type='mmdet.YOLOX',
            init_cfg=dict(
                type='Pretrained',
                checkpoint='/root/minio_model/1518897383465840642/epoch_19.pth'
            ),
            input_size=(640, 640),
            random_size_range=(15, 25),
            random_size_interval=10,
            backbone=dict(
                type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
            neck=dict(
                type='YOLOXPAFPN',
                in_channels=[128, 256, 512],
                out_channels=128,
                num_csp_blocks=1),
            bbox_head=dict(
                type='YOLOXHead',
                num_classes=1,
                in_channels=128,
                feat_channels=128),
            train_cfg=dict(
                assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
            test_cfg=dict(
                score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65))),
        teacher_trainable=False,
        components=[
            dict(
                student_module='neck.out_convs.0.conv',
                teacher_module='neck.out_convs.0.conv',
                losses=[
                    dict(
                        type='ChannelWiseDivergence',
                        name='loss_cwd_logits',
                        tau=1,
                        loss_weight=5)
                ]),
            dict(
                student_module='neck.out_convs.1.conv',
                teacher_module='neck.out_convs.1.conv',
                losses=[
                    dict(
                        type='ChannelWiseDivergence',
                        name='loss_cwd_logits',
                        tau=1,
                        loss_weight=5)
                ]),
            dict(
                student_module='neck.out_convs.2.conv',
                teacher_module='neck.out_convs.2.conv',
                losses=[
                    dict(
                        type='ChannelWiseDivergence',
                        name='loss_cwd_logits',
                        tau=1,
                        loss_weight=5)
                ])
        ]))

2022-06-20 07:27:45,022 - mmdet - INFO - Epoch [2][2/8]	lr: 6.250e-04, eta: 0:05:27, time: 0.702, data_time: 0.108, memory: 6444, student.loss_cls: 0.4989, student.loss_bbox: 4.8508, student.loss_obj: 19.1068, distiller.loss_cwd_logits.0: 0.2968, loss: 24.7533
Traceback (most recent call last):
  File "MMCV/train.py", line 52, in <module>
    main()
  File "MMCV/train.py", line 46, in main
    train_distill(args)
  File "/root/project/xbrain-ai/XbrainAiModels/MMCV/tools/train_distill.py", line 39, in train_distill
    train_mmdet(args, cfg)
  File "/root/project/xbrain-ai/XbrainAiModels/MMCV/tools/train_mmdet.py", line 43, in train_mmdet
    train_mmdet_model(
  File "/root/project/xbrain-ai/ThirdParty/MMCV/mmrazor/mmrazor/apis/mmdet/train.py", line 206, in train_mmdet_model
    runner.run(data_loader, cfg.workflow)
  File "/opt/conda/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
    self.run_iter(data_batch, train_mode=True, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 29, in run_iter
    outputs = self.model.train_step(data_batch, self.optimizer,
  File "/opt/conda/lib/python3.8/site-packages/mmcv/parallel/data_parallel.py", line 75, in train_step
    return self.module.train_step(*inputs[0], **kwargs[0])
  File "/root/project/xbrain-ai/ThirdParty/MMCV/mmrazor/mmrazor/models/algorithms/general_distill.py", line 49, in train_step
    distill_losses = self.distiller.compute_distill_loss(data)
  File "/root/project/xbrain-ai/ThirdParty/MMCV/mmrazor/mmrazor/models/distillers/single_teacher.py", line 240, in compute_distill_loss
    losses[loss_name] = loss_module(s_out, t_out)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/root/project/xbrain-ai/ThirdParty/MMCV/mmrazor/mmrazor/models/losses/cwd.py", line 41, in forward
    assert preds_S.shape[-2:] == preds_T.shape[-2:]
AssertionError