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open-mmlab / mmdetection

OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io
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AssertionError: Results do not correspond to current coco set #11739

Open xyb1314 opened 1 month ago

xyb1314 commented 1 month ago

This is my config file `base = [ '../base/datasets/sixray_detection.py', '../base/default_runtime.py' ]

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记录一下更改内容

第一，改上面的数据集

第二，将frozen_block=1注释掉

第三，RandomChoiceResize中的图片比例不符合320*320，需要更改

第四，类别数从80-->15

第五，由于只有一张显卡，所以auto_scale_lr的batch_size=2

第六，改为加载全部预训练模型load_from

后续可能需要更改学习率，num_workers,num_queries,以及多少轮测试与保存等

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train_batch_size=2

metainfo = {

'classes': ('Gun', 'Knife', 'Lighter', 'Battery', 'Pliers', 'Scissors', 'Wrench', 'Hammer', 'Screwdriver', 'Dart', 'Bat', 'Fireworks', 'Saw_blade',

'Razor_blade', 'Pressure_vessel'),

'palette': [

(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),

(106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70),

(0, 0, 192), (250, 170, 30), (100, 170, 30), (220, 220, 0),

(175, 116, 175), (250, 0, 30), (165, 42, 42)

]

}

model = dict( type='DINO', num_queries=85, # num_matching_queries 900 with_box_refine=True, as_two_stage=True, data_preprocessor=dict( type='DetDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True, pad_size_divisor=1), backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(1, 2, 3),

frozen_stages=1,

    norm_cfg=dict(type='BN', requires_grad=False),
    norm_eval=True,
    style='pytorch',
    init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')
),
neck=dict(
    type='ChannelMapper',
    in_channels=[512, 1024, 2048],
    kernel_size=1,
    out_channels=256,
    act_cfg=None,
    norm_cfg=dict(type='GN', num_groups=32),
    num_outs=4),
encoder=dict(
    num_layers=6,
    layer_cfg=dict(
        self_attn_cfg=dict(embed_dims=256, num_levels=4,
                           dropout=0.0),  # 0.1 for DeformDETR
        ffn_cfg=dict(
            embed_dims=256,
            feedforward_channels=2048,  # 1024 for DeformDETR
            ffn_drop=0.0))),  # 0.1 for DeformDETR
decoder=dict(
    num_layers=6,
    return_intermediate=True,
    layer_cfg=dict(
        self_attn_cfg=dict(embed_dims=256, num_heads=8,
                           dropout=0.0),  # 0.1 for DeformDETR
        cross_attn_cfg=dict(embed_dims=256, num_levels=4,
                            dropout=0.0),  # 0.1 for DeformDETR
        ffn_cfg=dict(
            embed_dims=256,
            feedforward_channels=2048,  # 1024 for DeformDETR
            ffn_drop=0.0)),  # 0.1 for DeformDETR
    post_norm_cfg=None),
positional_encoding=dict(
    num_feats=128,
    normalize=True,
    offset=0.0,  # -0.5 for DeformDETR
    temperature=20),  # 10000 for DeformDETR
bbox_head=dict(
    type='DINOHead',
    num_classes=6, # 改成了15类
    sync_cls_avg_factor=True,
    loss_cls=dict(
        type='FocalLoss',
        use_sigmoid=True,
        gamma=2.0,
        alpha=0.25,
        loss_weight=1.0),  # 2.0 in DeformDETR
    loss_bbox=dict(type='L1Loss', loss_weight=5.0),
    loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
dn_cfg=dict(  # TODO: Move to model.train_cfg ?
    label_noise_scale=0.5,
    box_noise_scale=1.0,  # 0.4 for DN-DETR
    group_cfg=dict(dynamic=True, num_groups=None,
                   num_dn_queries=100)),  # TODO: half num_dn_queries
# training and testing settings
train_cfg=dict(
    assigner=dict(
        type='HungarianAssigner',
        match_costs=[
            dict(type='FocalLossCost', weight=2.0),
            dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
            dict(type='IoUCost', iou_mode='giou', weight=2.0)
        ])),
test_cfg=dict(max_per_img=300))  # 100 for DeformDETR

train_pipeline, NOTE the img_scale and the Pad's size_divisor is different

from the default setting in mmdet.

train_pipeline = [ dict(type='LoadImageFromFile', backend_args={{base.backend_args}}), dict(type='LoadAnnotations', with_bbox=True), dict(type='RandomFlip', prob=0.5), dict( type='RandomChoice', transforms=[ [ dict( type='RandomChoiceResize', scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), (608, 1333), (640, 1333), (672, 1333), (704, 1333), (736, 1333), (768, 1333), (800, 1333)],

scales=[(192, 320), (205, 320), (218, 320), (230, 320),

                #         (243, 320), (256, 320), (269, 320), (282, 320),
                #         (294, 320), (307, 320), (320, 320)],
                keep_ratio=True)
        ],
        [
            dict(
                type='RandomChoiceResize',
                # The radio of all image in train dataset < 7
                # follow the original implement
                scales=[(400, 4200), (500, 4200), (600, 4200)],
                # scales=[(160, 1008), (200, 1008), (240, 1008)],

                keep_ratio=True),
            dict(
                type='RandomCrop',
                crop_type='absolute_range',
                 crop_size=(384, 600),
                # crop_size=(150, 200),  # 320/800*384 , 600/1333*320

                allow_negative_crop=True),
            dict(
                type='RandomChoiceResize',
                 scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
                        (608, 1333), (640, 1333), (672, 1333), (704, 1333),
                         (736, 1333), (768, 1333), (800, 1333)],
                # scales=[(192, 320), (205, 320), (218, 320), (230, 320),
                #         (243, 320), (256, 320), (269, 320), (282, 320),
                #         (294, 320), (307, 320), (320, 320)],
                keep_ratio=True)
        ]
    ]),
dict(type='PackDetInputs')

] train_dataloader = dict( batch_size=train_batch_size, dataset=dict( filter_cfg=dict(filter_empty_gt=False), pipeline=train_pipeline))

optimizer

optim_wrapper = dict( type='OptimWrapper', optimizer=dict( type='AdamW', lr=0.0002, # 0.0002 for DeformDETR weight_decay=0.0001), clip_grad=dict(max_norm=0.1, norm_type=2), paramwise_cfg=dict(custom_keys={'backbone': dict(lr_mult=0.1)}) ) # custom_keys contains sampling_offsets and reference_points in DeformDETR # noqa

learning policy

max_epochs = 12 train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)

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

param_scheduler = [ dict( type='MultiStepLR', begin=0, end=max_epochs, by_epoch=True, milestones=[11], gamma=0.1) ]

NOTE: auto_scale_lr is for automatically scaling LR,

USER SHOULD NOT CHANGE ITS VALUES.

base_batch_size = (8 GPUs) x (2 samples per GPU)

auto_scale_lr = dict(base_batch_size=train_batch_size)

load_from = 'E:\D2E\Projects\DINO_mmdet3\pretrained_model\dino-4scale_r50_8xb2-12e_coco_20221202_182705-55b2bba2.pth'`

and sixray_detection.py file

dataset settings

dataset_type = 'CocoDataset' data_root = 'D:/Dataset/SIXray/'
metainfo = { 'classes': ('Gun', 'Knife', 'Wrench','Pliers','Scissors','Hammer'), 'palette': [ (220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230), (106, 0, 228), (0, 60, 100) ] }

backend_args = None

train_pipeline = [ dict(type='LoadImageFromFile', backend_args=backend_args), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', scale=(320, 320), keep_ratio=True), dict(type='RandomFlip', prob=0.5), dict(type='PackDetInputs') ] test_pipeline = [ dict(type='LoadImageFromFile', backend_args=backend_args), dict(type='Resize', scale=(320, 320), keep_ratio=True),

If you don't have a gt annotation, delete the pipeline

dict(type='LoadAnnotations', with_bbox=True),
dict(
    type='PackDetInputs',
    meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
               'scale_factor'))

] train_dataloader = dict( batch_size=2, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), batch_sampler=dict(type='AspectRatioBatchSampler'), dataset=dict( type=dataset_type, metainfo=metainfo, data_root=data_root, ann_file='AnnotationCOCO/voc07_trainval.json', data_prefix=dict(img='VOCdata/VOCdevkit/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=train_pipeline, backend_args=backend_args)) val_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type=dataset_type, metainfo=metainfo, data_root=data_root, ann_file='AnnotationCOCO/voc07_trainval.json', data_prefix=dict(img='VOCdata/VOCdevkit/'), test_mode=True, pipeline=test_pipeline, backend_args=backend_args)) test_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type=dataset_type, metainfo=metainfo, data_root=data_root, ann_file='AnnotationCOCO/voc07_test.json', data_prefix=dict(img='VOCdata/VOCdevkit/'), test_mode=True, pipeline=test_pipeline, backend_args=backend_args))

val_evaluator = dict( type='CocoMetric', ann_file=data_root + 'AnnotationCOCO/voc07_val.json', metric='bbox', format_only=False, backend_args=backend_args) test_evaluator = val_evaluator

and error is below : 05/24 11:13:03 - mmengine - INFO - Epoch(val) [1][6400/7143] eta: 0:01:02 time: 0.0744 data_time: 0.0008 memory: 715 05/24 11:13:07 - mmengine - INFO - Epoch(val) [1][6450/7143] eta: 0:00:58 time: 0.0857 data_time: 0.0009 memory: 715 File "C:\Users\Administrator\miniconda3\envs\mmdet\lib\site-packages\mmengine\runner\loops.py", line 102, in run self.runner.val_loop.run() File "C:\Users\Administrator\miniconda3\envs\mmdet\lib\site-packages\mmengine\runner\loops.py", line 374, in run metrics = self.evaluator.evaluate(len(self.dataloader.dataset)) File "C:\Users\Administrator\miniconda3\envs\mmdet\lib\site-packages\mmengine\evaluator\evaluator.py", line 79, in evaluate _results = metric.evaluate(size) File "C:\Users\Administrator\miniconda3\envs\mmdet\lib\site-packages\mmengine\evaluator\metric.py", line 133, in evaluate _metrics = self.compute_metrics(results) # type: ignore File "c:\users\administrator\desktop\mmdetection-main\mmdet\evaluation\metrics\coco_metric.py", line 462, in compute_metrics coco_dt = self._coco_api.loadRes(predictions) File "C:\Users\Administrator\miniconda3\envs\mmdet\lib\site-packages\pycocotools\coco.py", line 327, in loadRes assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \ AssertionError: Results do not correspond to current coco set