Load weight mismatch

[CuongNN218]

@hyz-xmaster Hi, thanks for your great work. I'm using VFNetX with custom data set with 14 classes and get this warning. But when i use VFNet with R50 backbone with no DCN this warning disappeared. Is it normal ?

[CuongNN218]

`base = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' model = dict( pretrained='open-mmlab://res2net101_v1d_26w_4s', backbone=dict( type='Res2Net', depth=101, scales=4, base_width=26, 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', dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), stage_with_dcn=(False, True, True, True)), neck=dict( type='PAFPNX', in_channels=[256, 512, 1024, 2048], out_channels=384, start_level=1, add_extra_convs=True, extra_convs_on_inputs=False, # use P5 num_outs=5, relu_before_extra_convs=True, pafpn_conv_cfg=dict(type='DCNv2'), no_norm_on_lateral=True, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)), bbox_head=dict( type='VFNetHead', num_classes=14, in_channels=384, stacked_convs=4, feat_channels=384, strides=[8, 16, 32, 64, 128], regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512), (512, 1e8)), anchor_generator=dict( type='AnchorGenerator', ratios=[1.0], octave_base_scale=8, scales_per_octave=1, center_offset=0.0, strides=[8, 16, 32, 64, 128]), center_sampling=False, dcn_on_last_conv=True, use_atss=True, use_vfl=True, loss_cls=dict( type='VarifocalLoss', use_sigmoid=True, alpha=0.75, gamma=2.0, iou_weighted=True, loss_weight=1.0), loss_bbox=dict(type='GIoULoss', loss_weight=1.5), loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0)))

training and testing settings

train_cfg = dict( assigner=dict(type='ATSSAssigner', topk=9), 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='soft_nms', iou_threshold=0.65), max_per_img=100)

data setting

dataset_type = 'CocoDataset' data_root = '' dataset_type = 'CocoDataset' classes =('Aortic enlargement', 'Atelectasis', 'Calcification', 'Cardiomegaly', 'Consolidation', 'ILD', 'Infiltration', 'Lung Opacity', 'Nodule/Mass', 'Other lesion', 'Pleural effusion', 'Pleural thickening', 'Pneumothorax', 'Pulmonary fibrosis',)

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='RandomCrop', crop_type='relative_range', crop_size=(0.75, 0.75), crop_p=0.5), dict( type='Resize', img_scale=[(896, 896)], ratio_range=(0.75, 1.25), multiscale_mode='range', keep_ratio=True), dict( type='CutOut', n_holes=(5, 10), cutout_shape=[(4, 4), (4, 8), (8, 4), (8, 8), (16, 8), (8, 16), (16, 16), (16, 32), (32, 16), (32, 32), (32, 48), (48, 32), (48, 48)], cutout_p=0.5), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', img_norm_cfg), 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=[(1200, 1200), (1024, 1024), (896, 896), (640, 640)], flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=4, workers_per_gpu=4, train=dict( ann_file='/root/vinBigdata/mmdet_data/fold_0/annotations/train.json', classes=classes, img_prefix='/root/vinBigdata/mmdet_data/fold_0/train', pipeline=train_pipeline), val=dict(ann_file='/root/vinBigdata/mmdet_data/fold_0/annotations/val.json', classes=classes,
img_prefix='/root/vinBigdata/mmdet_data/fold_0/val', pipeline=test_pipeline), test=dict(ann_file='/root/vinBigdata/mmdet_data/fold_0/annotations/val.json', classes=classes, img_prefix='/root/vinBigdata/mmdet_data/fold_0/val', pipeline=test_pipeline))

optimizer

optimizer = dict( lr=0.005, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) optimizer_config = dict(grad_clip=None)

learning policy

lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.1, step=[36, 40]) total_epochs = 41

swa learning policy

swa_lr_config = dict( policy='cyclic', target_ratio=(1, 0.01), cyclic_times=18, step_ratio_up=0.0) swa_total_epochs = 18

runtime

load_from = './weights/SWA-VFNetX-1-18-53.4_VFNetX-R2101-41e-0.01-0.0001-52.2.pth' resume_from = None workflow = [('train', 3)]`

[CuongNN218]

above is my config file

[hyz-xmaster]

Hi @CuongNN218, what is your MMDetection version? lower than v2.4.0?

[CuongNN218]

@hyz-xmaster i used mmdetection 2.8.0 and use your repo to training. Can you check my config file for me ?

[hyz-xmaster]

Hi @CuongNN218, it is normal, because the original Res2Net has no DCN layers, so when you insert DCN layers, there is mismatch between the layer names of the original Res2Net and the DCN-based Res2Net. However, in the pre-trained VFNetX model which you are loading, there are DCN weights in the backbone, matching the DCN-based Res2Net.

[hyz-xmaster]

Regarding your config file, it seems fine. I am just curious about why you set workflow = [('train', 3)] since it is usual to set workflow = [('train', 1)].

[CuongNN218]

@hyz-xmaster https://mmdetection.readthedocs.io/en/latest/tutorials/customize_runtime.html#customize-workflow you can read it in the link below. maybe i'm wrong about validation after 3 epochs

[hyz-xmaster]

@CuongNN218, you can add evaluation = dict(interval=3, metric='bbox') to the config file to enable evaluation on the val set after 3 training epochs. This will overwrite the default evaluation = dict(interval=1, metric='bbox').