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open-mmlab / mmdetection

OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io
Apache License 2.0
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Issues replicating mmdetection v2 results with mmdetection v3 #11621

Open reiffd7 opened 7 months ago

reiffd7 commented 7 months ago

I've been working on migrating an mmdetection from v2 to v3 and haven't been able reproduce the same results. Here is my configuration from v2:

model = dict(
    type='RetinaNet',
    backbone=dict(
        type='RegNet',
        arch='regnetx_400mf',
        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='open-mmlab://regnetx_400mf')),
    neck=dict(
        type='FPN',
        in_channels=[32, 64, 160, 384],
        out_channels=256,
        start_level=1,
        add_extra_convs=True,
        num_outs=5),
    bbox_head=dict(
        type='ATSSHead',
        num_classes=1,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        norm_cfg=None,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            octave_base_scale=8,
            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]),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=1.5,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
    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=16,
        score_thr=0.25,
        nms=dict(type='nms', iou_threshold=0.5),
        max_per_img=300))

img_norm_cfg = dict(
    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)

albu_train_transforms = [
    dict(
        type="OneOf",
        transforms=[
            dict(type="RandomResizedCrop", p=0.33, height=544, width=960, scale=(0.5, 1), ratio=(0.5, 3), interpolation=1),
            dict(type="RandomResizedCrop", p=0.66, height=544, width=960, scale=(1, 1), ratio=(1, 1), interpolation=1),
        ],
        p=1
    ),
    dict(
        type='OneOf',
        transforms=[
            dict(type='Rotate', p=0.33, limit=(-30, 30), interpolation=1, border_mode=2),
            dict(type='VerticalFlip', p=0.1),
        ],
        p=0.66),
    dict(
        type='OneOf',
        transforms=[
            dict(type='RGBShift', p=0.5, r_shift_limit=(-30, 30), g_shift_limit=(-30, 30), b_shift_limit=(-30, 30)), 
            dict(type='RandomBrightnessContrast', p=0.5, brightness_limit=(-0.5, 0.5), contrast_limit=(-0.33, 0.33)), 
            dict(type='RandomGamma', p=0.5, gamma_limit=(80, 120)),
            dict(type='ToGray', p=0.5),
        ],
        p=0.66),
    dict(
        type='OneOf',
        transforms=[
            dict(type='MultiplicativeNoise', p=0.5, multiplier=(0.8, 1.2)),
            dict(type='Spatter', p=0.5, mean=0.65, std=0.3, gauss_sigma=2, cutout_threshold=0.68, intensity=0.2, mode='rain'), 
            dict(type='GaussNoise', p=0.5, var_limit=(10, 40), mean=0),
            dict(type='ISONoise', p=0.5, color_shift=(0.01, 0.05), intensity=(0.1, 0.3)),
        ],
        p=0.66),

    dict(
        type='OneOf',
        transforms=[
            dict(type='Perspective', p=0.5, interpolation=1, keep_size=True),
            dict(type='MotionBlur', p=0.5, blur_limit=7),
            dict(type='GaussianBlur', p=0.5),
            dict(
                type='ImageCompression',
                p=0.33,
                quality_lower=50,
                quality_upper=100)
        ],
        p=0.5)
]

data = dict(
    samples_per_gpu=20,
    workers_per_gpu=4,
    train=dict(
        type='CocoDataset',
        ann_file='/workspace/datasets/person_detection_03_04_2024_train/labels.json',
        img_prefix='/workspace/datasets/person_detection_03_04_2024_train/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Resize',
                img_scale=[(1920, 1080), (1080, 1080),(720,720),(544,544),(960,544)],
                multiscale_mode='value',
                keep_ratio=True),
            dict(
                type='Albu',
                transforms=albu_train_transforms,
                bbox_params=dict(
                    type='BboxParams',
                    format='pascal_voc',
                    label_fields=['gt_labels'],
                    min_visibility=0.5,
                    min_area=128,
                    check_each_transform=True,
                    filter_lost_elements=True),
                update_pad_shape=False,
                skip_img_without_anno=False),
            dict(
                type='Normalize',
                mean=[103.53, 116.28, 123.675],
                std=[57.375, 57.12, 58.395],
                to_rgb=False),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        filter_empty_gt=False,
        classes=['person']),
    val=dict(
        type='CocoDataset',
        ann_file='/workspace/datasets/person_detection_03_04_2024_val/labels.json',
        img_prefix='/workspace/datasets/person_detection_03_04_2024_val/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=[(960, 544)],
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(
                        type='Normalize',
                        mean=[103.53, 116.28, 123.675],
                        std=[57.375, 57.12, 58.395],
                        to_rgb=False),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        filter_empty_gt=False,
        classes=['person']),
    test=dict(
        type='CocoDataset',
        ann_file='/workspace/datasets/person_detection_03_04_2024_test/labels.json',
        img_prefix='/workspace/datasets/person_detection_03_04_2024_test/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=[(1280, 720)],
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(
                        type='Normalize',
                        mean=[103.53, 116.28, 123.675],
                        std=[57.375, 57.12, 58.395],
                        to_rgb=False),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        filter_empty_gt=False,
        classes=['person']))

evaluation = dict(interval=5, metric=['bbox'], classwise=True)

optimizer = dict(
    type='AdamW',
    lr=0.0001,
    weight_decay=0.05,
    paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True))

optimizer_config = dict(
    grad_clip=dict(max_norm=25, norm_type=2))

lr_config = dict(
    policy='cyclic',
    target_ratio=(2.0, 1.0),
    cyclic_times=1,
    step_ratio_up=0.2,
    gamma=0.6,
    warmup='linear',
    warmup_iters=50,
    warmup_ratio=0.1)

momentum_config = dict(
    policy='cyclic',
    target_ratio=(0.9, 1),
    cyclic_times=1,
    step_ratio_up=0.3)

runner = dict(type='EpochBasedRunner', max_epochs=64)
checkpoint_config = dict(interval=1)
log_config = dict(
    interval=1,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='TensorboardLoggerHook'),
    ])

resume_from = None
load_from = None

custom_hooks = [
    dict(type='NaNHook', interval=1),
    dict(
        type='ExpMomentumEMAHook',
        resume_from=resume_from,
        momentum=0.0001,
        priority=49)
]
dist_params = dict(backend='nccl')

log_level = 'INFO'

workflow = [('train', 5), ('val', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'

base_batch_size = 110
auto_scale_lr = dict(enable=False, base_batch_size=base_batch_size)

custom_imports = dict(
    imports=['mmdet.core.utils.nan_hook'], allow_failed_imports=False)

seed = 0
auto_resume = False

classes = ['person']
train_dataset = dict(
    fiftyone_dataset_name='person_detection_03_04_2024_train',
    train_data_config=dict(
        type='CocoDataset',
        img_prefix='/workspace/datasets/person_detection_03_04_2024_train/',
        ann_file='/workspace/datasets/person_detection_03_04_2024_train/labels.json',
        classes=classes,
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Resize',
                img_scale=[(1920, 1080), (1080, 1080),(720,720),(544,544),(960,544)],
                multiscale_mode='value',
                keep_ratio=True),
            dict(
                type='Albu',
                transforms=albu_train_transforms),
            dict(
                type='Normalize',
                mean=[103.53, 116.28, 123.675],
                std=[57.375, 57.12, 58.395],
                to_rgb=False),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        filter_empty_gt=False))
val_dataset = dict(
    fiftyone_dataset_name='person_detection_03_04_2024_val',
    val_data_config=dict(
        type='CocoDataset',
        img_prefix='/workspace/datasets/person_detection_03_04_2024_val/',
        ann_file='/workspace/datasets/person_detection_03_04_2024_val/labels.json',
        classes=classes,
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=[(960, 544)],
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(
                        type='Normalize',
                        mean=[103.53, 116.28, 123.675],
                        std=[57.375, 57.12, 58.395],
                        to_rgb=False),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        filter_empty_gt=False,
))
test_dataset = dict(
    fiftyone_dataset_name='person_detection_03_04_2024_test',
    test_data_config=dict(
        type='CocoDataset',
        img_prefix='/workspace/datasets/person_detection_03_04_2024_test/',
        ann_file='/workspace/datasets/person_detection_03_04_2024_test/labels.json',
        classes=classes,
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=[(1280, 720)],
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(
                        type='Normalize',
                        mean=[103.53, 116.28, 123.675],
                        std=[57.375, 57.12, 58.395],
                        to_rgb=False),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        filter_empty_gt=False,
))

work_dir = './work_dirs/retinanet_regnetx_800mf_fpn_1x8_1x_person_collection'
gpu_ids = [0]
fp16 = dict(loss_scale='dynamic')

Using this configuration I was able to achieve nearly 70% mAP@0.50.

Screen Shot 2024-04-08 at 12 30 30 PM Screen Shot 2024-04-08 at 12 29 22 PM Screen Shot 2024-04-08 at 12 28 30 PM

Next, I replicated this experiment using v3:

model = dict(
    type='RetinaNet',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[103.53, 116.28, 123.675],
        std=[57.375, 57.12, 58.395],
        bgr_to_rgb=False,
        pad_size_divisor=32),
    backbone=dict(
        type='RegNet',
        arch='regnetx_400mf',
        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='open-mmlab://regnetx_400mf')
    ),
    neck=dict(
        type='FPN',
        in_channels=[32, 64, 160, 384],
        out_channels=256,
        start_level=1,
        add_extra_convs=True,
        num_outs=5),
    bbox_head=dict(
        type='ATSSHead',
        num_classes=1,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        norm_cfg=None,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            octave_base_scale=8,
            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]),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=1.5,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
    # model 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=16,
        score_thr=0.25,
        nms=dict(type='nms', iou_threshold=0.5),
        max_per_img=300))

# dataset settings
dataset_type = 'CocoDataset'
data_root = '/home/ubuntu/mmdetection-v3/'
backend_args = None

albu_train_transforms = [
    dict(
        type="OneOf",
        transforms=[
            dict(type="RandomResizedCrop", p=0.33, height=544, width=960, scale=(0.5, 1), ratio=(0.5, 3), interpolation=1),
            dict(type="RandomResizedCrop", p=0.66, height=544, width=960, scale=(1, 1), ratio=(1, 1), interpolation=1),
        ],
        p=1
    ),
    dict(
        type='OneOf',
        transforms=[
            dict(type='Rotate', p=0.33, limit=(-30, 30), interpolation=1, border_mode=2),
            dict(type='VerticalFlip', p=0.1),
        ],
        p=0.66),
    dict(
        type='OneOf',
        transforms=[
            dict(type='RGBShift', p=0.5, r_shift_limit=(-30, 30), g_shift_limit=(-30, 30), b_shift_limit=(-30, 30)), 
            dict(type='RandomBrightnessContrast', p=0.5, brightness_limit=(-0.5, 0.5), contrast_limit=(-0.33, 0.33)), 
            dict(type='RandomGamma', p=0.5, gamma_limit=(80, 120)),
            dict(type='ToGray', p=0.5),
        ],
        p=0.66),
    dict(
        type='OneOf',
        transforms=[
            dict(type='MultiplicativeNoise', p=0.5, multiplier=(0.8, 1.2)),
            dict(type='Spatter', p=0.5, mean=0.65, std=0.3, gauss_sigma=2, cutout_threshold=0.68, intensity=0.2, mode='rain'), 
            dict(type='GaussNoise', p=0.5, var_limit=(10, 40), mean=0),
            dict(type='ISONoise', p=0.5, color_shift=(0.01, 0.05), intensity=(0.1, 0.3)),
        ],
        p=0.66),
    dict(
        type='OneOf',
        transforms=[
            dict(type='Perspective', p=0.5, interpolation=1, keep_size=True),
            dict(type='MotionBlur', p=0.5, blur_limit=7),
            dict(type='GaussianBlur', p=0.5),
            dict(
                type='ImageCompression',
                p=0.33,
                quality_lower=50,
                quality_upper=100)
        ],
        p=0.5)
]

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='RandomFlip', prob=0.5),
    dict(
    type='RandomChoiceResize',
    scales=[(1920, 1080), (1080, 1080),(720,720),(544,544),(960,544)],
    keep_ratio=True),
    dict(
                type='Albu',
                transforms=albu_train_transforms,
                bbox_params=dict(
                    type='BboxParams',
                    format='pascal_voc',
                    label_fields=['gt_bboxes_labels', 'gt_ignore_flags'],
                    min_visibility=0.5,
                    min_area=128,
                    check_each_transform=True,
                    ),
                keymap={
                    'img': 'image',
                    'gt_bboxes': 'bboxes'
                },
                # update_pad_shape=False,
                skip_img_without_anno=True),
    dict(
        type='PackDetInputs')
]

test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=backend_args),
    dict(type='Resize', scale=(960, 544), keep_ratio=True),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Pad', size_divisor=32),
    dict(
        type='PackDetInputs',
        meta_keys=('img_path', 'img_id', 'seg_map_path', 'height', 'width', 'instances', 'sample_idx', 'img',
                   'img_shape', 'ori_shape', 'scale', 'scale_factor', 'keep_ratio', 'homography_matrix',
                   'gt_bboxes', 'gt_ignore_flags', 'gt_bboxes_labels'))
]

batch_size = 20
num_workers = 4

train_dataloader = dict(
    batch_size=batch_size,
    num_workers=num_workers,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='data/person_detection_03_04_2024_train/labels.json',
        data_prefix=dict(
            img='data/person_detection_03_04_2024_train/data/'),
        filter_cfg=dict(filter_empty_gt=False, min_size=32),
        pipeline=train_pipeline,
        backend_args=backend_args))

val_dataloader = dict(
    batch_size=batch_size,
    num_workers=num_workers,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='data/person_detection_03_04_2024_val/labels.json',
        data_prefix=dict(
            img='data/person_detection_03_04_2024_val/data/'),
        test_mode=True,
        pipeline=test_pipeline,
        backend_args=backend_args))

test_dataloader = val_dataloader

val_evaluator = dict(
    type='CocoMetric',
    ann_file='data/person_detection_03_04_2024_val/labels.json',
    metric=['bbox'],
    format_only=False,
    backend_args=backend_args)

test_evaluator = val_evaluator

train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=64, val_interval=5)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(
        type='AdamW',
        lr=0.0002,
        weight_decay=0.05,
        eps=1e-8,
        betas=(0.9, 0.999)),

    clip_grad=dict(max_norm=25, norm_type=2),

    paramwise_cfg=dict(
        custom_keys={
            'backbone': dict(lr_mult=0.1, decay_mult=1.0),
        },
        norm_decay_mult=0.0),

)

param_scheduler = dict(
    type="OneCycleLR", eta_max=0.0002, pct_start=0.2, div_factor=2,  by_epoch=False
)

default_scope = 'mmdet'

default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=2),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='DetVisualizationHook',
                       draw=True,
                       interval=5,
                       show=False)
)

custom_hooks = [
    dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW'),
]

env_cfg = dict(
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'),
)

vis_backends = [
    dict(type='LocalVisBackend'),
    dict(type='TensorboardVisBackend'),
]

visualizer = dict(
    type='DetLocalVisualizer',
    vis_backends=vis_backends,
    name='visualizer',
    save_dir='data/logs/')

log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)

log_level = 'INFO'
load_from = None
resume = False

gpu_ids = [0]
fp16 = dict(loss_scale='dynamic')

With this configuration, I achieved 60% mAP@0.50. Screen Shot 2024-04-08 at 12 33 16 PM Screen Shot 2024-04-08 at 12 33 54 PM

I believe I have correctly migrated aspects of the configuration according to https://mmdetection.readthedocs.io/en/latest/migration/config_migration.html such as

Something I find curious is that the v2 experiment mAP immediately (after 5 epochs) very high while in the v3 experiment it starts very low and gradually improves. I thought that this could be due to issues reading the checkpoint file in v3. I tried manually downloading the checkpoint file and replacing the checkpoint url with my local filepath but this didn't change anything. Additionally, I removed the checkpoint file entirely and the performance was significantly worse so I don't think this is causing the issue.

Has anyone else experienced issues replicating performance while migrating from v2 to v3? Any help would be greatly appreciated!!

Y-T-G commented 6 months ago

Is this one custom dataset? I am facing similar issue.

CFZ1 commented 2 months ago

I've been working on migrating an mmdetection from v2 to v3 and haven't been able reproduce the same results. Here is my configuration from v2:

model = dict(
    type='RetinaNet',
    backbone=dict(
        type='RegNet',
        arch='regnetx_400mf',
        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='open-mmlab://regnetx_400mf')),
    neck=dict(
        type='FPN',
        in_channels=[32, 64, 160, 384],
        out_channels=256,
        start_level=1,
        add_extra_convs=True,
        num_outs=5),
    bbox_head=dict(
        type='ATSSHead',
        num_classes=1,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        norm_cfg=None,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            octave_base_scale=8,
            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]),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=1.5,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
    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=16,
        score_thr=0.25,
        nms=dict(type='nms', iou_threshold=0.5),
        max_per_img=300))

img_norm_cfg = dict(
    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)

albu_train_transforms = [
    dict(
        type="OneOf",
        transforms=[
            dict(type="RandomResizedCrop", p=0.33, height=544, width=960, scale=(0.5, 1), ratio=(0.5, 3), interpolation=1),
            dict(type="RandomResizedCrop", p=0.66, height=544, width=960, scale=(1, 1), ratio=(1, 1), interpolation=1),
        ],
        p=1
    ),
    dict(
        type='OneOf',
        transforms=[
            dict(type='Rotate', p=0.33, limit=(-30, 30), interpolation=1, border_mode=2),
            dict(type='VerticalFlip', p=0.1),
        ],
        p=0.66),
    dict(
        type='OneOf',
        transforms=[
            dict(type='RGBShift', p=0.5, r_shift_limit=(-30, 30), g_shift_limit=(-30, 30), b_shift_limit=(-30, 30)), 
            dict(type='RandomBrightnessContrast', p=0.5, brightness_limit=(-0.5, 0.5), contrast_limit=(-0.33, 0.33)), 
            dict(type='RandomGamma', p=0.5, gamma_limit=(80, 120)),
            dict(type='ToGray', p=0.5),
        ],
        p=0.66),
    dict(
        type='OneOf',
        transforms=[
            dict(type='MultiplicativeNoise', p=0.5, multiplier=(0.8, 1.2)),
            dict(type='Spatter', p=0.5, mean=0.65, std=0.3, gauss_sigma=2, cutout_threshold=0.68, intensity=0.2, mode='rain'), 
            dict(type='GaussNoise', p=0.5, var_limit=(10, 40), mean=0),
            dict(type='ISONoise', p=0.5, color_shift=(0.01, 0.05), intensity=(0.1, 0.3)),
        ],
        p=0.66),

    dict(
        type='OneOf',
        transforms=[
            dict(type='Perspective', p=0.5, interpolation=1, keep_size=True),
            dict(type='MotionBlur', p=0.5, blur_limit=7),
            dict(type='GaussianBlur', p=0.5),
            dict(
                type='ImageCompression',
                p=0.33,
                quality_lower=50,
                quality_upper=100)
        ],
        p=0.5)
]

data = dict(
    samples_per_gpu=20,
    workers_per_gpu=4,
    train=dict(
        type='CocoDataset',
        ann_file='/workspace/datasets/person_detection_03_04_2024_train/labels.json',
        img_prefix='/workspace/datasets/person_detection_03_04_2024_train/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Resize',
                img_scale=[(1920, 1080), (1080, 1080),(720,720),(544,544),(960,544)],
                multiscale_mode='value',
                keep_ratio=True),
            dict(
                type='Albu',
                transforms=albu_train_transforms,
                bbox_params=dict(
                    type='BboxParams',
                    format='pascal_voc',
                    label_fields=['gt_labels'],
                    min_visibility=0.5,
                    min_area=128,
                    check_each_transform=True,
                    filter_lost_elements=True),
                update_pad_shape=False,
                skip_img_without_anno=False),
            dict(
                type='Normalize',
                mean=[103.53, 116.28, 123.675],
                std=[57.375, 57.12, 58.395],
                to_rgb=False),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        filter_empty_gt=False,
        classes=['person']),
    val=dict(
        type='CocoDataset',
        ann_file='/workspace/datasets/person_detection_03_04_2024_val/labels.json',
        img_prefix='/workspace/datasets/person_detection_03_04_2024_val/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=[(960, 544)],
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(
                        type='Normalize',
                        mean=[103.53, 116.28, 123.675],
                        std=[57.375, 57.12, 58.395],
                        to_rgb=False),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        filter_empty_gt=False,
        classes=['person']),
    test=dict(
        type='CocoDataset',
        ann_file='/workspace/datasets/person_detection_03_04_2024_test/labels.json',
        img_prefix='/workspace/datasets/person_detection_03_04_2024_test/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=[(1280, 720)],
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(
                        type='Normalize',
                        mean=[103.53, 116.28, 123.675],
                        std=[57.375, 57.12, 58.395],
                        to_rgb=False),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        filter_empty_gt=False,
        classes=['person']))

evaluation = dict(interval=5, metric=['bbox'], classwise=True)

optimizer = dict(
    type='AdamW',
    lr=0.0001,
    weight_decay=0.05,
    paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True))

optimizer_config = dict(
    grad_clip=dict(max_norm=25, norm_type=2))

lr_config = dict(
    policy='cyclic',
    target_ratio=(2.0, 1.0),
    cyclic_times=1,
    step_ratio_up=0.2,
    gamma=0.6,
    warmup='linear',
    warmup_iters=50,
    warmup_ratio=0.1)

momentum_config = dict(
    policy='cyclic',
    target_ratio=(0.9, 1),
    cyclic_times=1,
    step_ratio_up=0.3)

runner = dict(type='EpochBasedRunner', max_epochs=64)
checkpoint_config = dict(interval=1)
log_config = dict(
    interval=1,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='TensorboardLoggerHook'),
    ])

resume_from = None
load_from = None

custom_hooks = [
    dict(type='NaNHook', interval=1),
    dict(
        type='ExpMomentumEMAHook',
        resume_from=resume_from,
        momentum=0.0001,
        priority=49)
]
dist_params = dict(backend='nccl')

log_level = 'INFO'

workflow = [('train', 5), ('val', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'

base_batch_size = 110
auto_scale_lr = dict(enable=False, base_batch_size=base_batch_size)

custom_imports = dict(
    imports=['mmdet.core.utils.nan_hook'], allow_failed_imports=False)

seed = 0
auto_resume = False

classes = ['person']
train_dataset = dict(
    fiftyone_dataset_name='person_detection_03_04_2024_train',
    train_data_config=dict(
        type='CocoDataset',
        img_prefix='/workspace/datasets/person_detection_03_04_2024_train/',
        ann_file='/workspace/datasets/person_detection_03_04_2024_train/labels.json',
        classes=classes,
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Resize',
                img_scale=[(1920, 1080), (1080, 1080),(720,720),(544,544),(960,544)],
                multiscale_mode='value',
                keep_ratio=True),
            dict(
                type='Albu',
                transforms=albu_train_transforms),
            dict(
                type='Normalize',
                mean=[103.53, 116.28, 123.675],
                std=[57.375, 57.12, 58.395],
                to_rgb=False),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        filter_empty_gt=False))
val_dataset = dict(
    fiftyone_dataset_name='person_detection_03_04_2024_val',
    val_data_config=dict(
        type='CocoDataset',
        img_prefix='/workspace/datasets/person_detection_03_04_2024_val/',
        ann_file='/workspace/datasets/person_detection_03_04_2024_val/labels.json',
        classes=classes,
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=[(960, 544)],
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(
                        type='Normalize',
                        mean=[103.53, 116.28, 123.675],
                        std=[57.375, 57.12, 58.395],
                        to_rgb=False),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        filter_empty_gt=False,
))
test_dataset = dict(
    fiftyone_dataset_name='person_detection_03_04_2024_test',
    test_data_config=dict(
        type='CocoDataset',
        img_prefix='/workspace/datasets/person_detection_03_04_2024_test/',
        ann_file='/workspace/datasets/person_detection_03_04_2024_test/labels.json',
        classes=classes,
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=[(1280, 720)],
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(
                        type='Normalize',
                        mean=[103.53, 116.28, 123.675],
                        std=[57.375, 57.12, 58.395],
                        to_rgb=False),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        filter_empty_gt=False,
))

work_dir = './work_dirs/retinanet_regnetx_800mf_fpn_1x8_1x_person_collection'
gpu_ids = [0]
fp16 = dict(loss_scale='dynamic')

Using this configuration I was able to achieve nearly 70% mAP@0.50.

Screen Shot 2024-04-08 at 12 30 30 PM Screen Shot 2024-04-08 at 12 29 22 PM Screen Shot 2024-04-08 at 12 28 30 PM

Next, I replicated this experiment using v3:

model = dict(
    type='RetinaNet',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[103.53, 116.28, 123.675],
        std=[57.375, 57.12, 58.395],
        bgr_to_rgb=False,
        pad_size_divisor=32),
    backbone=dict(
        type='RegNet',
        arch='regnetx_400mf',
        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='open-mmlab://regnetx_400mf')
    ),
    neck=dict(
        type='FPN',
        in_channels=[32, 64, 160, 384],
        out_channels=256,
        start_level=1,
        add_extra_convs=True,
        num_outs=5),
    bbox_head=dict(
        type='ATSSHead',
        num_classes=1,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        norm_cfg=None,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            octave_base_scale=8,
            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]),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=1.5,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
    # model 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=16,
        score_thr=0.25,
        nms=dict(type='nms', iou_threshold=0.5),
        max_per_img=300))

# dataset settings
dataset_type = 'CocoDataset'
data_root = '/home/ubuntu/mmdetection-v3/'
backend_args = None

albu_train_transforms = [
    dict(
        type="OneOf",
        transforms=[
            dict(type="RandomResizedCrop", p=0.33, height=544, width=960, scale=(0.5, 1), ratio=(0.5, 3), interpolation=1),
            dict(type="RandomResizedCrop", p=0.66, height=544, width=960, scale=(1, 1), ratio=(1, 1), interpolation=1),
        ],
        p=1
    ),
    dict(
        type='OneOf',
        transforms=[
            dict(type='Rotate', p=0.33, limit=(-30, 30), interpolation=1, border_mode=2),
            dict(type='VerticalFlip', p=0.1),
        ],
        p=0.66),
    dict(
        type='OneOf',
        transforms=[
            dict(type='RGBShift', p=0.5, r_shift_limit=(-30, 30), g_shift_limit=(-30, 30), b_shift_limit=(-30, 30)), 
            dict(type='RandomBrightnessContrast', p=0.5, brightness_limit=(-0.5, 0.5), contrast_limit=(-0.33, 0.33)), 
            dict(type='RandomGamma', p=0.5, gamma_limit=(80, 120)),
            dict(type='ToGray', p=0.5),
        ],
        p=0.66),
    dict(
        type='OneOf',
        transforms=[
            dict(type='MultiplicativeNoise', p=0.5, multiplier=(0.8, 1.2)),
            dict(type='Spatter', p=0.5, mean=0.65, std=0.3, gauss_sigma=2, cutout_threshold=0.68, intensity=0.2, mode='rain'), 
            dict(type='GaussNoise', p=0.5, var_limit=(10, 40), mean=0),
            dict(type='ISONoise', p=0.5, color_shift=(0.01, 0.05), intensity=(0.1, 0.3)),
        ],
        p=0.66),
    dict(
        type='OneOf',
        transforms=[
            dict(type='Perspective', p=0.5, interpolation=1, keep_size=True),
            dict(type='MotionBlur', p=0.5, blur_limit=7),
            dict(type='GaussianBlur', p=0.5),
            dict(
                type='ImageCompression',
                p=0.33,
                quality_lower=50,
                quality_upper=100)
        ],
        p=0.5)
]

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='RandomFlip', prob=0.5),
    dict(
    type='RandomChoiceResize',
    scales=[(1920, 1080), (1080, 1080),(720,720),(544,544),(960,544)],
    keep_ratio=True),
    dict(
                type='Albu',
                transforms=albu_train_transforms,
                bbox_params=dict(
                    type='BboxParams',
                    format='pascal_voc',
                    label_fields=['gt_bboxes_labels', 'gt_ignore_flags'],
                    min_visibility=0.5,
                    min_area=128,
                    check_each_transform=True,
                    ),
                keymap={
                    'img': 'image',
                    'gt_bboxes': 'bboxes'
                },
                # update_pad_shape=False,
                skip_img_without_anno=True),
    dict(
        type='PackDetInputs')
]

test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=backend_args),
    dict(type='Resize', scale=(960, 544), keep_ratio=True),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Pad', size_divisor=32),
    dict(
        type='PackDetInputs',
        meta_keys=('img_path', 'img_id', 'seg_map_path', 'height', 'width', 'instances', 'sample_idx', 'img',
                   'img_shape', 'ori_shape', 'scale', 'scale_factor', 'keep_ratio', 'homography_matrix',
                   'gt_bboxes', 'gt_ignore_flags', 'gt_bboxes_labels'))
]

batch_size = 20
num_workers = 4

train_dataloader = dict(
    batch_size=batch_size,
    num_workers=num_workers,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='data/person_detection_03_04_2024_train/labels.json',
        data_prefix=dict(
            img='data/person_detection_03_04_2024_train/data/'),
        filter_cfg=dict(filter_empty_gt=False, min_size=32),
        pipeline=train_pipeline,
        backend_args=backend_args))

val_dataloader = dict(
    batch_size=batch_size,
    num_workers=num_workers,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='data/person_detection_03_04_2024_val/labels.json',
        data_prefix=dict(
            img='data/person_detection_03_04_2024_val/data/'),
        test_mode=True,
        pipeline=test_pipeline,
        backend_args=backend_args))

test_dataloader = val_dataloader

val_evaluator = dict(
    type='CocoMetric',
    ann_file='data/person_detection_03_04_2024_val/labels.json',
    metric=['bbox'],
    format_only=False,
    backend_args=backend_args)

test_evaluator = val_evaluator

train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=64, val_interval=5)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(
        type='AdamW',
        lr=0.0002,
        weight_decay=0.05,
        eps=1e-8,
        betas=(0.9, 0.999)),

    clip_grad=dict(max_norm=25, norm_type=2),

    paramwise_cfg=dict(
        custom_keys={
            'backbone': dict(lr_mult=0.1, decay_mult=1.0),
        },
        norm_decay_mult=0.0),

)

param_scheduler = dict(
    type="OneCycleLR", eta_max=0.0002, pct_start=0.2, div_factor=2,  by_epoch=False
)

default_scope = 'mmdet'

default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=2),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='DetVisualizationHook',
                       draw=True,
                       interval=5,
                       show=False)
)

custom_hooks = [
    dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW'),
]

env_cfg = dict(
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'),
)

vis_backends = [
    dict(type='LocalVisBackend'),
    dict(type='TensorboardVisBackend'),
]

visualizer = dict(
    type='DetLocalVisualizer',
    vis_backends=vis_backends,
    name='visualizer',
    save_dir='data/logs/')

log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)

log_level = 'INFO'
load_from = None
resume = False

gpu_ids = [0]
fp16 = dict(loss_scale='dynamic')

With this configuration, I achieved 60% mAP@0.50. Screen Shot 2024-04-08 at 12 33 16 PM Screen Shot 2024-04-08 at 12 33 54 PM

I believe I have correctly migrated aspects of the configuration according to https://mmdetection.readthedocs.io/en/latest/migration/config_migration.html such as

* image normalization

* data transformations like the RandomChoiceResize operation

* optimizer

Something I find curious is that the v2 experiment mAP immediately (after 5 epochs) very high while in the v3 experiment it starts very low and gradually improves. I thought that this could be due to issues reading the checkpoint file in v3. I tried manually downloading the checkpoint file and replacing the checkpoint url with my local filepath but this didn't change anything. Additionally, I removed the checkpoint file entirely and the performance was significantly worse so I don't think this is causing the issue.

Has anyone else experienced issues replicating performance while migrating from v2 to v3? Any help would be greatly appreciated!!

If you have found the reason or any potential solution, I would greatly appreciate your help!

CFZ1 commented 2 months ago

Is this one custom dataset? I am facing similar issue.

Why does this phenomenon occur? Have you found the reason? If you could tell me, I would be extremely grateful

Y-T-G commented 2 months ago

@CFZ1 I had this issue with the VOC dataset and it was because of boxes getting filtered based on minimum size.

https://github.com/open-mmlab/mmdetection/issues/10502#issuecomment-1593020683

CFZ1 commented 2 months ago

@CFZ1 I had this issue with the VOC dataset and it was because of boxes getting filtered based on minimum size.

#10502 (comment)

Thanks a lot.