[Bug]Distll for point_rend

[xuhao-anhe]

Describe the bug

A clear and concise description of what the bug is.

[here]I try to use CWD for point_ Rend for distillation.It has the following bugs：Traceback (most recent call last): File "tools/mmdet/train_mmdet.py", line 210, in main() File "tools/mmdet/train_mmdet.py", line 206, in main meta=meta) File "/media/jidong/code/xuhao/mmrazor-master/mmrazor/apis/mmdet/train.py", line 206, in train_mmdet_model runner.run(data_loader, cfg.workflow) File "/home/jidong/anaconda3/envs/mmrazor/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 130, in run epoch_runner(data_loaders[i], **kwargs) File "/home/jidong/anaconda3/envs/mmrazor/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 51, in train self.run_iter(data_batch, train_mode=True, **kwargs) File "/home/jidong/anaconda3/envs/mmrazor/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 30, in run_iter **kwargs) File "/home/jidong/anaconda3/envs/mmrazor/lib/python3.7/site-packages/mmcv/parallel/data_parallel.py", line 75, in train_step return self.module.train_step(*inputs[0], **kwargs[0]) File "/media/jidong/code/xuhao/mmrazor-master/mmrazor/models/algorithms/general_distill.py", line 49, in train_step distill_losses = self.distiller.compute_distill_loss(data) File "/media/jidong/code/xuhao/mmrazor-master/mmrazor/models/distillers/single_teacher.py", line 240, in compute_distill_loss losses[loss_name] = loss_module(s_out, t_out) File "/home/jidong/anaconda3/envs/mmrazor/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, **kwargs) File "/media/jidong/code/xuhao/mmrazor-master/mmrazor/models/losses/cwd.py", line 50, in forward logsoftmax(preds_S.view(-1, W * H) / self.tau)) * ( RuntimeError: The size of tensor a (220) must match the size of tensor b (12) at non-singleton dimension 0

To Reproduce

The command you executed.

[here]python tools/mmdet/train_mmdet.py configs/distill/cwd/pointrend.py --work-dir test/ --cfg-options algorithm.distiller.teacher.init_cfg.checkpoint=https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth

Post related information

1. The output of pip list | grep "mmcv\|mmrazor\|^torch" [here]
2. Your config file if you modified it or created a new one.

[here]_base_ = [
    '../../_base_/datasets/mmdet/coco_instance.py',
    '../../_base_/schedules/mmdet/schedule_1x.py',
    '../../_base_/mmdet_runtime.py'
]

# model settings
student = dict(
    type='mmdet.PointRend',
    backbone=dict(
        type='ResNet',
        depth=18,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=False),
        norm_eval=True,
        style='caffe'
        # init_cfg=dict(
        #     type='Pretrained',
        #     checkpoint='open-mmlab://detectron2/resnet18_caffe')
    ),
    neck=dict(
        type='FPN',
        in_channels=[64, 128, 256, 512],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='PointRendRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=4,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0., 0., 0., 0.],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
        mask_roi_extractor=dict(
            type='GenericRoIExtractor',
            aggregation='concat',
            roi_layer=dict(
                type='SimpleRoIAlign', output_size=14),
            out_channels=256,
            featmap_strides=[4]),
        mask_head=dict(
            type='CoarseMaskHead',
            num_fcs=2,
            in_channels=256,
            conv_out_channels=256,
            fc_out_channels=1024,
            num_classes=4,
            loss_mask=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
        point_head=dict(
            type='MaskPointHead',
            num_fcs=3,
            in_channels=256,
            fc_channels=256,
            num_classes=4,
            coarse_pred_each_layer=True,
            loss_point=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
    # model training and testing settings
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            mask_size=7,
            num_points=14 * 14,
            oversample_ratio=3,
            importance_sample_ratio=0.75,
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,  # 在nms之前保留的的得分最高的proposal数量
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100,
            mask_thr_binary=0.5,
            subdivision_steps=5,
            subdivision_num_points=28 * 28,
            scale_factor=2)))

checkpoint = '/media/jidong/code/xuhao/mmdetection/load/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth'

teacher = dict(
    type='mmdet.PointRend',
    init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=False),
        norm_eval=True,
        style='caffe',
        init_cfg=dict(
            type='Pretrained',
            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='PointRendRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=4,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0., 0., 0., 0.],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
        mask_roi_extractor=dict(
            type='GenericRoIExtractor',
            aggregation='concat',
            roi_layer=dict(
                type='SimpleRoIAlign', output_size=14),
            out_channels=256,
            featmap_strides=[4]),
        mask_head=dict(
            type='CoarseMaskHead',
            num_fcs=2,
            in_channels=256,
            conv_out_channels=256,
            fc_out_channels=1024,
            num_classes=4,
            loss_mask=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
        point_head=dict(
            type='MaskPointHead',
            num_fcs=3,
            in_channels=256,
            fc_channels=256,
            num_classes=4,
            coarse_pred_each_layer=True,
            loss_point=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
    # model training and testing settings
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            mask_size=7,
            num_points=14 * 14,
            oversample_ratio=3,
            importance_sample_ratio=0.75,
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,  # 在nms之前保留的的得分最高的proposal数量
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            # nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05),
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100,
            mask_thr_binary=0.5,
            subdivision_steps=5,
            subdivision_num_points=28 * 28,
            scale_factor=2)))

algorithm = dict(
    type='GeneralDistill',
    architecture=dict(
        type='MMDetArchitecture',
        model=student,
    ),
    distiller=dict(
        type='SingleTeacherDistiller',
        teacher=teacher,
        teacher_trainable=False,
        components=[
            dict(
                student_module='roi_head.mask_head',
                teacher_module='roi_head.mask_head',
                losses=[
                    dict(
                        type='ChannelWiseDivergence',
                        name='loss_cwd_logits',
                        tau=1,
                        loss_weight=5,
                    )
                ])
        ]),
)

find_unused_parameters = True

3. Your train log file if you meet the problem during training. [here]
4. Other code you modified in the mmrazor folder. [here]

Additional context

Add any other context about the problem here.

[here]

[xuhao-anhe]

Excuse me, can anyone solve this problem

[pppppM]

Sorry for replying to you so late.

It seems to be caused by the different results of the ROI bbox head, not a bug. CWD loss requires two inputs of the same size, you can try to adjust the position where cwd loss is added.

[xuhao-anhe]

Thank you for your reply， I will check it.