When training Retinanet shows The testing results of the whole dataset is empty.

[freedomspiritJohn]

I'm using a custom dataset in CoCo format. I have tried a small learning rate, but it is useless.But my dataset can be trained on Faster RCNN and Centernet (In order to save energy, I directly modified in the COCO format file)

this is confifg.py

auto_scale_lr = dict(base_batch_size=16, enable=False)
backend_args = None
batch_size = 30
classes = (
    'bottle',
    'cable',
    'capsule',
    'carpet',
    'grid',
    'hazelnut',
    'leather',
    'metal_nut',
    'pill',
    'screw',
    'tile',
    'toothbrush',
    'transistor',
    'wood',
    'zipper',
)
data_root = 'data/mvtecad/'
dataset_type = 'CocoDataset'
default_hooks = dict(
    checkpoint=dict(interval=1, type='CheckpointHook'),
    logger=dict(interval=50, type='LoggerHook'),
    param_scheduler=dict(type='ParamSchedulerHook'),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    timer=dict(type='IterTimerHook'),
    visualization=dict(type='DetVisualizationHook'))
default_scope = 'mmdet'
env_cfg = dict(
    cudnn_benchmark=False,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
launcher = 'none'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
model = dict(
    backbone=dict(
        depth=18,
        frozen_stages=1,
        init_cfg=dict(checkpoint='torchvision://resnet18', type='Pretrained'),
        norm_cfg=dict(requires_grad=True, type='BN'),
        norm_eval=True,
        num_stages=4,
        out_indices=(
            0,
            1,
            2,
            3,
        ),
        style='pytorch',
        type='ResNet'),
    bbox_head=dict(
        anchor_generator=dict(
            octave_base_scale=4,
            ratios=[
                0.5,
                1.0,
                2.0,
            ],
            scales_per_octave=3,
            strides=[
                8,
                16,
                32,
                64,
                128,
            ],
            type='AnchorGenerator'),
        bbox_coder=dict(
            target_means=[
                0.0,
                0.0,
                0.0,
                0.0,
            ],
            target_stds=[
                1.0,
                1.0,
                1.0,
                1.0,
            ],
            type='DeltaXYWHBBoxCoder'),
        feat_channels=256,
        in_channels=256,
        loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
        loss_cls=dict(
            alpha=0.25,
            gamma=2.0,
            loss_weight=1.0,
            type='FocalLoss',
            use_sigmoid=True),
        num_classes=15,
        stacked_convs=4,
        type='RetinaHead'),
    data_preprocessor=dict(
        bgr_to_rgb=True,
        mean=[
            123.675,
            116.28,
            103.53,
        ],
        pad_size_divisor=32,
        std=[
            58.395,
            57.12,
            57.375,
        ],
        type='DetDataPreprocessor'),
    neck=dict(
        add_extra_convs='on_input',
        in_channels=[
            64,
            128,
            256,
            512,
        ],
        num_outs=5,
        out_channels=256,
        start_level=1,
        type='FPN'),
    test_cfg=dict(
        max_per_img=100,
        min_bbox_size=0,
        nms=dict(iou_threshold=0.5, type='nms'),
        nms_pre=1000,
        score_thr=0.05),
    train_cfg=dict(
        allowed_border=-1,
        assigner=dict(
            ignore_iof_thr=-1,
            min_pos_iou=0,
            neg_iou_thr=0.4,
            pos_iou_thr=0.5,
            type='MaxIoUAssigner'),
        debug=False,
        pos_weight=-1,
        sampler=dict(type='PseudoSampler')),
    type='RetinaNet')
optim_wrapper = dict(
    loss_scale='dynamic',
    optimizer=dict(lr=0.01, momentum=0.01, type='SGD', weight_decay=0.0001),
    type='AmpOptimWrapper')
param_scheduler = [
    dict(
        begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
    dict(
        begin=0,
        by_epoch=True,
        end=12,
        gamma=0.1,
        milestones=[
            8,
            11,
        ],
        type='MultiStepLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='annotations/val.json',
        backend_args=None,
        data_prefix=dict(img='val/'),
        data_root='data/mvtecad/',
        metainfo=dict(
            classes=(
                'bottle',
                'cable',
                'capsule',
                'carpet',
                'grid',
                'hazelnut',
                'leather',
                'metal_nut',
                'pill',
                'screw',
                'tile',
                'toothbrush',
                'transistor',
                'wood',
                'zipper',
            )),
        pipeline=[
            dict(backend_args=None, type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                1024,
                1024,
            ), type='Resize'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                meta_keys=(
                    'img_id',
                    'img_path',
                    'ori_shape',
                    'img_shape',
                    'scale_factor',
                ),
                type='PackDetInputs'),
        ],
        test_mode=True,
        type='CocoDataset'),
    drop_last=False,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
    ann_file='data/mvtecad/annotations/val.json',
    backend_args=None,
    format_only=False,
    metric='bbox',
    type='CocoMetric')
test_pipeline = [
    dict(backend_args=None, type='LoadImageFromFile'),
    dict(keep_ratio=True, scale=(
        1024,
        1024,
    ), type='Resize'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        meta_keys=(
            'img_id',
            'img_path',
            'ori_shape',
            'img_shape',
            'scale_factor',
        ),
        type='PackDetInputs'),
]
train_cfg = dict(max_epochs=2, type='EpochBasedTrainLoop', val_interval=1)
train_dataloader = dict(
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    batch_size=30,
    dataset=dict(
        ann_file='annotations/train.json',
        backend_args=None,
        data_prefix=dict(img='train/'),
        data_root='data/mvtecad/',
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        metainfo=dict(
            classes=(
                'bottle',
                'cable',
                'capsule',
                'carpet',
                'grid',
                'hazelnut',
                'leather',
                'metal_nut',
                'pill',
                'screw',
                'tile',
                'toothbrush',
                'transistor',
                'wood',
                'zipper',
            )),
        pipeline=[
            dict(backend_args=None, type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(keep_ratio=True, scale=(
                1024,
                1024,
            ), type='Resize'),
            dict(prob=0.5, type='RandomFlip'),
            dict(type='PackDetInputs'),
        ],
        type='CocoDataset'),
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
    dict(backend_args=None, type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(keep_ratio=True, scale=(
        1024,
        1024,
    ), type='Resize'),
    dict(prob=0.5, type='RandomFlip'),
    dict(type='PackDetInputs'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
    batch_size=30,
    dataset=dict(
        ann_file='annotations/val.json',
        backend_args=None,
        data_prefix=dict(img='val/'),
        data_root='data/mvtecad/',
        metainfo=dict(
            classes=(
                'bottle',
                'cable',
                'capsule',
                'carpet',
                'grid',
                'hazelnut',
                'leather',
                'metal_nut',
                'pill',
                'screw',
                'tile',
                'toothbrush',
                'transistor',
                'wood',
                'zipper',
            )),
        pipeline=[
            dict(backend_args=None, type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                1024,
                1024,
            ), type='Resize'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                meta_keys=(
                    'img_id',
                    'img_path',
                    'ori_shape',
                    'img_shape',
                    'scale_factor',
                ),
                type='PackDetInputs'),
        ],
        test_mode=True,
        type='CocoDataset'),
    drop_last=False,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
    ann_file='data/mvtecad/annotations/val.json',
    backend_args=None,
    format_only=False,
    metric='bbox',
    type='CocoMetric')
vis_backends = [
    dict(type='LocalVisBackend'),
]
visualizer = dict(
    name='visualizer',
    type='DetLocalVisualizer',
    vis_backends=[
        dict(type='LocalVisBackend'),
    ])
work_dir = './work_dirs\\retinanet_r18_fpn_1x_coco'

this is retinanet_r18_fpn_1x_coco.py

_base_ = [
    '../_base_/models/retinanet_r50_fpn.py',
    '../_base_/datasets/coco_detection.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]

# model
model = dict(
    backbone=dict(
        depth=18,
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
    bbox_head=dict(num_classes=15),
    neck=dict(in_channels=[64, 128, 256, 512]))

optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=0.00001, momentum=0.01, weight_decay=0.0001))

train_cfg = dict(max_epochs=2, val_interval=1)

# optim_wrapper = dict(
#     type='AmpOptimWrapper',
#     optimizer=dict(
#         type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00004))

batch_size = 30
train_dataloader = dict(batch_size=batch_size)
val_dataloader = dict(batch_size=batch_size)
# TODO: support auto scaling lr
# 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=16)

this is coco_detection.py

# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/mvtecad/'
classes = ('bottle', 'cable', 'capsule', 'carpet', 'grid', 'hazelnut', 'leather', 'metal_nut', 'pill', 'screw', 'tile', 'toothbrush', 'transistor', 'wood', 'zipper')
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)

# data_root = 's3://openmmlab/datasets/detection/coco/'

# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         './data/': 's3://openmmlab/datasets/detection/',
#         'data/': 's3://openmmlab/datasets/detection/'
#     }))
backend_args = None

train_pipeline = [
    dict(type='LoadImageFromFile', backend_args=backend_args),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', scale=(900, 900), 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=(900, 900), 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,
        # 将类别名字添加至 `metainfo` 字段中
        metainfo=dict(classes=classes),
        data_root=data_root,
        ann_file='annotations/train.json',
        data_prefix=dict(img='train/'),
        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,
        # 将类别名字添加至 `metainfo` 字段中
        metainfo=dict(classes=classes),
        data_root=data_root,
        ann_file='annotations/val.json',
        data_prefix=dict(img='val/'),
        test_mode=True,
        pipeline=test_pipeline,
        backend_args=backend_args))
test_dataloader = val_dataloader

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

[w577658669]

Have you solve this issue?

[freedomspiritJohn]

Have you solve this issue?

no

[SongPool]

the ann["categories"] should start from 1, not 0,