将v8的骨干网络替换成SwinTransformer-Tiny网络，然后报错了RuntimeError: Given groups=1, weight of size [192, 576, 1, 1], expected input[12, 1152, 40, 40] to have 576 channels, but got 1152 channels instead

[wuningaixiaoteng]

Prerequisite

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• [X] The bug has not been fixed in the latest version.

🐞 Describe the bug

这是要修改的配置文件，已经将骨干网络替换了，但是报错了，RuntimeError: Given groups=1, weight of size [192, 576, 1, 1], expected input[12, 1152, 40, 40] to have 576 channels, but got 1152 channels instead，这个问题应该如何修改？以下是我的配置文件

_base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']

# ========================Frequently modified parameters经常修改的参数======================
# -----data related-----
data_root = '/raid/wuning/data/' 

train_ann_file = '/raid/wuning/data/annotations_coco/trainval.json'
train_data_prefix = '/raid/wuning/data/JPEGImages/'  

val_ann_file = '/raid/wuning/data/annotations_coco/test.json'
val_data_prefix = '/raid/wuning/data/JPEGImages/' 

class_name = ('jyzt','jyz','jyzx','xjt','bgxj' )
num_classes = len(class_name)
metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
# Batch size of a single GPU during training
train_batch_size_per_gpu = 12
# Worker to pre-fetch data for each single GPU during training
train_num_workers = 4

load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_s_syncbn_fast_8xb16-500e_coco/yolov8_s_syncbn_fast_8xb16-500e_coco_20230117_180101-5aa5f0f1.pth'  # noqa

# 如果num_workers为0，则persistent_workers必须为False
persistent_workers = True

# -----train val related训练验证集相关-----
base_lr = 0.01
max_epochs = 40  
# 最后是个epochs禁用马赛克增强
close_mosaic_epochs = 5

model_test_cfg = dict(
    # 多标签多类预测的配置
    multi_label=True,
    #NMS前的框的数量
    nms_pre=30000,
    score_thr=0.001,  # 过滤框的阈值
    nms=dict(type='nms', iou_threshold=0.7),  # NMS类型和阈值
    max_per_img=300)  # 每张图片最大检测数目

# ========================Possible modified parameters可能修改的参数========================
# -----data related-----
img_scale = (640, 640)  # width, height
# 数据集类型，用于定义数据集
dataset_type = 'YOLOv5CocoDataset'
# 单个GPU在验证期间的批量大小
val_batch_size_per_gpu = 1
# Worker to pre-fetch data for each single GPU during validation
val_num_workers = 2

# Config of batch shapes. Only on val.
# We tested YOLOv8-m will get 0.02 higher than not using it.
batch_shapes_cfg = None
# You can turn on `batch_shapes_cfg` by uncommenting the following lines.
# batch_shapes_cfg = dict(
#     type='BatchShapePolicy',
#     batch_size=val_batch_size_per_gpu,
#     img_size=img_scale[0],
#     # The image scale of padding should be divided by pad_size_divisor
#     size_divisor=32,
#     # Additional paddings for pixel scale
#     extra_pad_ratio=0.5)

# -----model related-----
# The scaling factor that controls the depth of the network structure
deepen_factor = 0.33
# The scaling factor that controls the width of the network structure
widen_factor = 0.5
# Strides of multi-scale prior box
strides = [8, 16, 32]
# The output channel of the last stage
last_stage_out_channels = 1024
num_det_layers = 3  # The number of model output scales
norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)  # Normalization config

# -----train val related-----
affine_scale = 0.5  # YOLOv5RandomAffine scaling ratio
# YOLOv5RandomAffine aspect ratio of width and height thres to filter bboxes
max_aspect_ratio = 100
tal_topk = 10  # Number of bbox selected in each level
tal_alpha = 0.5  # A Hyper-parameter related to alignment_metrics
tal_beta = 6.0  # A Hyper-parameter related to alignment_metrics
# TODO: Automatically scale loss_weight based on number of detection layers
loss_cls_weight = 0.5
loss_bbox_weight = 7.5
# Since the dfloss is implemented differently in the official
# and mmdet, we're going to divide loss_weight by 4.
loss_dfl_weight = 1.5 / 4
lr_factor = 0.01  # Learning rate scaling factor
weight_decay = 0.0005
# Save model checkpoint and validation intervals in stage 1
save_epoch_intervals = 10
# validation intervals in stage 2
val_interval_stage2 = 1
# The maximum checkpoints to keep.
max_keep_ckpts = 2
# Single-scale training is recommended to
# be turned on, which can speed up training.
env_cfg = dict(cudnn_benchmark=True)

channels = [192, 384, 768]
#channels = [256, 512, 1024]
checkpoint_file = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'  # noqa

# ===============================Unmodified in most cases====================
model = dict(
    type='YOLODetector',
    data_preprocessor=dict(
        type='YOLOv5DetDataPreprocessor',
        mean=[0., 0., 0.],
        std=[255., 255., 255.],
        bgr_to_rgb=True),
    backbone=dict(
        # _delete_=True, # 将 _base_ 中关于 backbone 的字段删除
        type='mmdet.SwinTransformer', # 使用 mmdet 中的 SwinTransformer
        embed_dims=96,
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24],
        window_size=7,
        mlp_ratio=4,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.,
        attn_drop_rate=0.,
        drop_path_rate=0.2,
        patch_norm=True,
        out_indices=(1, 2, 3),
        with_cp=False,
        convert_weights=True,
        init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)),
    neck=dict(
        type='YOLOv8PAFPN',
        deepen_factor=deepen_factor,
        widen_factor=widen_factor,
        in_channels=channels,#last_stage_out_channels
        out_channels=channels,#last_stage_out_channels
        num_csp_blocks=3,
        norm_cfg=norm_cfg,
        act_cfg=dict(type='SiLU', inplace=True)),
    bbox_head=dict(
        type='YOLOv8Head',
        head_module=dict(
            type='YOLOv8HeadModule',
            num_classes=num_classes,
            in_channels=channels,#last_stage_out_channels
            widen_factor=widen_factor,
            reg_max=16,
            norm_cfg=norm_cfg,
            act_cfg=dict(type='SiLU', inplace=True),
            featmap_strides=strides),
        prior_generator=dict(
            type='mmdet.MlvlPointGenerator', offset=0.5, strides=strides),
        bbox_coder=dict(type='DistancePointBBoxCoder'),
        # scaled based on number of detection layers
        loss_cls=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=True,
            reduction='none',
            loss_weight=loss_cls_weight),
        loss_bbox=dict(
            type='IoULoss',
            iou_mode='ciou',
            bbox_format='xyxy',
            reduction='sum',
            loss_weight=loss_bbox_weight,
            return_iou=False),
        loss_dfl=dict(
            type='mmdet.DistributionFocalLoss',
            reduction='mean',
            loss_weight=loss_dfl_weight)),
#训练和测试的配置
    train_cfg=dict(
        assigner=dict(
            type='BatchTaskAlignedAssigner',
            num_classes=num_classes,
            use_ciou=True,
            topk=tal_topk,
            alpha=tal_alpha,
            beta=tal_beta,
            eps=1e-9)),
    test_cfg=model_test_cfg)

#数据集和评测器配置
albu_train_transforms = [
    dict(type='Blur', p=0.01),
    dict(type='MedianBlur', p=0.01),
    dict(type='ToGray', p=0.01),
    dict(type='CLAHE', p=0.01)
]

pre_transform = [
    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
    dict(type='LoadAnnotations', with_bbox=True)
]

last_transform = [
    dict(
        type='mmdet.Albu',
        transforms=albu_train_transforms,
        bbox_params=dict(
            type='BboxParams',
            format='pascal_voc',
            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
        keymap={
            'img': 'image',
            'gt_bboxes': 'bboxes'
        }),
    dict(type='YOLOv5HSVRandomAug'),
    dict(type='mmdet.RandomFlip', prob=0.5),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
                   'flip_direction'))
]

train_pipeline = [
    *pre_transform,
    dict(
        type='Mosaic',
        img_scale=img_scale,
        pad_val=114.0,
        pre_transform=pre_transform),
    dict(
        type='YOLOv5RandomAffine',
        max_rotate_degree=0.0,
        max_shear_degree=0.0,
        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
        max_aspect_ratio=max_aspect_ratio,
        # img_scale is (width, height)
        border=(-img_scale[0] // 2, -img_scale[1] // 2),
        border_val=(114, 114, 114)),
    *last_transform
]

train_pipeline_stage2 = [
    *pre_transform,
    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
    dict(
        type='LetterResize',
        scale=img_scale,
        allow_scale_up=True,
        pad_val=dict(img=114.0)),
    dict(
        type='YOLOv5RandomAffine',
        max_rotate_degree=0.0,
        max_shear_degree=0.0,
        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
        max_aspect_ratio=max_aspect_ratio,
        border_val=(114, 114, 114)), *last_transform
]

train_dataloader = dict(
    batch_size=train_batch_size_per_gpu,
    num_workers=train_num_workers,
    persistent_workers=persistent_workers,
    pin_memory=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    collate_fn=dict(type='yolov5_collate'),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        metainfo=metainfo,#####################
        ann_file=train_ann_file,
        data_prefix=dict(img=train_data_prefix),
        filter_cfg=dict(filter_empty_gt=False, min_size=32),
        pipeline=train_pipeline))

test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
    dict(
        type='LetterResize',
        scale=img_scale,
        allow_scale_up=False,
        pad_val=dict(img=114)),
    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor', 'pad_param'))
]

val_dataloader = dict(
    batch_size=val_batch_size_per_gpu,
    num_workers=val_num_workers,
    persistent_workers=persistent_workers,
    pin_memory=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        metainfo=metainfo,######################之前没有
        data_root=data_root,
        test_mode=True,
        data_prefix=dict(img=val_data_prefix),
        ann_file=val_ann_file,
        pipeline=test_pipeline,
        batch_shapes_cfg=batch_shapes_cfg))

test_dataloader = val_dataloader

#优化相关配置
param_scheduler = None
optim_wrapper = dict(
    type='OptimWrapper',#优化器封装的类型
    clip_grad=dict(max_norm=10.0),#梯度剪裁
    optimizer=dict(#优化器配置
        type='SGD',
        lr=base_lr,
        momentum=0.937,
        weight_decay=weight_decay,
        nesterov=True,
        batch_size_per_gpu=train_batch_size_per_gpu),
    constructor='YOLOv5OptimizerConstructor')#优化器构建器

#钩子配置
default_hooks = dict(
    param_scheduler=dict(
        type='YOLOv5ParamSchedulerHook',
        scheduler_type='linear',
        lr_factor=lr_factor,
        max_epochs=max_epochs,
        warmup_mim_iter=10),################
    logger=dict(type='LoggerHook', interval=5),#####################
    checkpoint=dict(
        type='CheckpointHook',
        interval=save_epoch_intervals,
        save_best='auto',
        max_keep_ckpts=max_keep_ckpts))

custom_hooks = [
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0001,
        update_buffers=True,
        strict_load=False,
        priority=49),
    dict(
        type='mmdet.PipelineSwitchHook',
        switch_epoch=max_epochs - close_mosaic_epochs,
        switch_pipeline=train_pipeline_stage2)
]

#评测器配置
val_evaluator = dict(
    type='mmdet.CocoMetric',
    proposal_nums=(100, 1, 10),
    ann_file='/raid/wuning/data/annotations_coco/test.json',
    metric='bbox')
test_evaluator = val_evaluator

train_cfg = dict(
    type='EpochBasedTrainLoop',
    max_epochs=max_epochs,
    val_interval=save_epoch_intervals,
    dynamic_intervals=[((max_epochs - close_mosaic_epochs),
                        val_interval_stage2)])

val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

Environment

conda create -n mmyolo python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y conda activate mmyolo pip install openmim mim install "mmengine>=0.6.0" mim install "mmcv>=2.0.0rc4,<2.1.0" mim install "mmdet>=3.0.0,<4.0.0" git clone https://github.com/open-mmlab/mmyolo.git cd mmyolo

Install albumentations

pip install -r requirements/albu.txt

Install MMYOLO

mim install -v -e .

Additional information

想知道在哪里修改

[RangeKing]

@wuningaixiaoteng widen_factor = 0.5，Neck 的输入通道数为你设置的 channels = [192, 384, 768] * 0.5。将 channels 改为 [384, 768, 1536] 即可正常训练。