<filename>configs/tood/tood_r101_fpn_12e_voc0712_cocofmt.py
_base_ = [
    '../_base_/datasets/voc0712.py', '../_base_/default_runtime.py'
]
model = dict(
    type='TOOD',
    backbone=dict(
        type='ResNet',
        depth=101,
        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',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_output',
        num_outs=5),
    bbox_head=dict(
        type='TOODHead',
        num_classes=20,
        in_channels=256,
        stacked_convs=6,
        feat_channels=256,
        anchor_type='anchor_free',
        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],
            target_stds=[0.1, 0.1, 0.2, 0.2]),
        initial_loss_cls=dict(
            type='FocalLossWithProb',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_cls=dict(
            type='TaskAlignedFocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
    ),
    # training and testing settings
    train_cfg = dict(
        initial_epoch=4,
        initial_assigner=dict(type='ATSSAssigner', topk=9),
        assigner=dict(type='TaskAlignedAssigner', topk=13),
        alpha=1,
        beta=6,
        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='nms', iou_threshold=0.6),
        max_per_img=100))

CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
           'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
           'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')

# dataset settings
dataset_type = 'CocoDataset'
data_root = '/home/amax/VOCdevkit/'
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='Resize', img_scale=(1000, 600), keep_ratio=True),
    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=(1000, 600),
        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='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='RepeatDataset',
        times=1,
        dataset=dict(
            type=dataset_type,
            ann_file=data_root+'voc0712_trainval.json',
            img_prefix=data_root,
            pipeline=train_pipeline,
            classes=CLASSES)),
    val=dict(
        type=dataset_type,
        ann_file=data_root+'voc07_test.json',
        img_prefix=data_root,
        pipeline=test_pipeline,
        classes=CLASSES),
    test=dict(
        type=dataset_type,
        ann_file=data_root+'voc07_test.json',
        img_prefix=data_root,
        pipeline=test_pipeline,
        classes=CLASSES))
evaluation = dict(interval=1, metric='bbox')
# optimizer
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))

# custon hooks: HeadHook is defined in mmdet/core/utils/head_hook.py
custom_hooks = [
    dict(type="HeadHook")
]
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8,11])
# runtime settings
runner = dict(
    type='EpochBasedRunner', max_epochs=12)  # actual epoch = 4 * 3 = 12
work_dir = 'work_dirs/voc/tood/tood_r101_fpn_12e_voc0712_cocofmt'

<filename>configs/tood/tood_r101_fpn_12e_voc0712_cocofmt.py
_base_ = [
    '../_base_/datasets/voc0712.py', '../_base_/default_runtime.py'
]
model = dict(
    type='TOOD',
    backbone=dict(
        type='ResNet',
        depth=101,
        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',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_output',
        num_outs=5),
    bbox_head=dict(
        type='TOODHead',
        num_classes=20,
        in_channels=256,
        stacked_convs=6,
        feat_channels=256,
        anchor_type='anchor_free',
        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],
            target_stds=[0.1, 0.1, 0.2, 0.2]),
        initial_loss_cls=dict(
            type='FocalLossWithProb',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_cls=dict(
            type='TaskAlignedFocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
    ),
    # training and testing settings
    train_cfg = dict(
        initial_epoch=4,
        initial_assigner=dict(type='ATSSAssigner', topk=9),
        assigner=dict(type='TaskAlignedAssigner', topk=13),
        alpha=1,
        beta=6,
        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='nms', iou_threshold=0.6),
        max_per_img=100))

CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
           'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
           'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')

# dataset settings
dataset_type = 'CocoDataset'
data_root = '/home/amax/VOCdevkit/'
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='Resize', img_scale=(1000, 600), keep_ratio=True),
    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=(1000, 600),
        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='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='RepeatDataset',
        times=1,
        dataset=dict(
            type=dataset_type,
            ann_file=data_root+'voc0712_trainval.json',
            img_prefix=data_root,
            pipeline=train_pipeline,
            classes=CLASSES)),
    val=dict(
        type=dataset_type,
        ann_file=data_root+'voc07_test.json',
        img_prefix=data_root,
        pipeline=test_pipeline,
        classes=CLASSES),
    test=dict(
        type=dataset_type,
        ann_file=data_root+'voc07_test.json',
        img_prefix=data_root,
        pipeline=test_pipeline,
        classes=CLASSES))
evaluation = dict(interval=1, metric='bbox')
# optimizer
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))

# custon hooks: HeadHook is defined in mmdet/core/utils/head_hook.py
custom_hooks = [
    dict(type="HeadHook")
]
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8,11])
# runtime settings
runner = dict(
    type='EpochBasedRunner', max_epochs=12)  # actual epoch = 4 * 3 = 12
work_dir = 'work_dirs/voc/tood/tood_r101_fpn_12e_voc0712_cocofmt'

# training and testing settings # dataset settings # optimizer # custon hooks: HeadHook is defined in mmdet/core/utils/head_hook.py # learning policy # runtime settings # actual epoch = 4 * 3 = 12