i changed the number of ratios, then model can not train ,where should i have to modify futher?

[joeyslv]

i modify the ratios=[1] to ratios=[2.444, 3.182, 1.574, 1.721, 0.994, 1.163, 0.751, 0.534] then have a error like this：

2022-03-29 15:12:23,844 - mmdet - INFO - workflow: [('train', 1)], max: 100 epochs
2022-03-29 15:12:23,844 - mmdet - INFO - Checkpoints will be saved to E:\Object-Detection\Github\radar-detection\work_dirs\radar_tood by HardDiskBackend.
D:\App\anaconda\envs\swin-t\lib\site-packages\torch\nn\functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  ..\c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
Traceback (most recent call last):
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmcv\runner\epoch_based_runner.py", line 50, in train
    self.run_iter(data_batch, train_mode=True, **kwargs)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmcv\runner\epoch_based_runner.py", line 30, in run_iter
    **kwargs)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmcv\parallel\data_parallel.py", line 75, in train_step
    return self.module.train_step(*inputs[0], **kwargs[0])
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmdet\models\detectors\base.py", line 248, in train_step
    losses = self(**data)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmcv\runner\fp16_utils.py", line 98, in new_func
    return old_func(*args, **kwargs)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmdet\models\detectors\base.py", line 172, in forward
    return self.forward_train(img, img_metas, **kwargs)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmdet\models\detectors\single_stage.py", line 84, in forward_train
    gt_labels, gt_bboxes_ignore)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmdet\models\dense_heads\base_dense_head.py", line 330, in forward_train
    outs = self(x)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmdet\models\dense_heads\tood_head.py", line 263, in forward
    b, h, w, 4).permute(0, 3, 1, 2) / stride[0]
RuntimeError: shape '[8, 32, 168, 4]' is invalid for input of size 1376256

and this is my config file

dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=8,
    workers_per_gpu=1,
    train=dict(
        type='CocoDataset',
        ann_file='E:/Object-Detection/data_radar/devkit/voc07_train.json',
        img_prefix='E:/Object-Detection/data_radar/devkit/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        classes=('loose_l', 'loose_s', 'poor_l', 'porous')),
    val=dict(
        type='CocoDataset',
        ann_file='E:/Object-Detection/data_radar/devkit/voc07_val.json',
        img_prefix='E:/Object-Detection/data_radar/devkit/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=('loose_l', 'loose_s', 'poor_l', 'porous')),
    test=dict(
        type='CocoDataset',
        ann_file='E:/Object-Detection/data_radar/devkit/voc07_test.json',
        img_prefix='E:/Object-Detection/data_radar/devkit/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=('loose_l', 'loose_s', 'poor_l', 'porous')))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=100)
checkpoint_config = dict(interval=10)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='SetEpochInfoHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
model = dict(
    type='TOOD',
    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=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_output',
        num_outs=5),
    bbox_head=dict(
        type='TOODHead',
        num_classes=4,
        in_channels=256,
        stacked_convs=6,
        feat_channels=256,
        anchor_type='anchor_based',
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[2.444, 3.182, 1.574, 1.721, 0.994, 1.163, 0.751, 0.534],
            octave_base_scale=1,
            scales_per_octave=1,
            strides=[8, 16, 32, 64, 128]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[0.1, 0.1, 0.2, 0.2]),
        initial_loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            activated=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_cls=dict(
            type='QualityFocalLoss',
            use_sigmoid=True,
            activated=True,
            beta=2.0,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
    train_cfg=dict(
        initial_epoch=4,
        initial_assigner=dict(type='ATSSAssigner', topk=9),
        assigner=dict(type='TaskAlignedAssigner', topk=13),
        alpha=1,
        beta=6,
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type='nms', iou_threshold=0.6),
        max_per_img=100))
classes = ('loose_l', 'loose_s', 'poor_l', 'porous')
work_dir = './work_dirs\radar_tood'
auto_resume = False
gpu_ids = [0]