mmdetection/configs/convnext/cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py

_base_ = [
    '../_base_/models/cascade-mask-rcnn_r50_fpn.py',
    '../_base_/datasets/coco_instance.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]

# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth'  # noqa

model = dict(
    backbone=dict(
        _delete_=True,
        type='mmcls.ConvNeXt',
        arch='tiny',
        out_indices=[0, 1, 2, 3],
        drop_path_rate=0.4,
        layer_scale_init_value=1.0,
        gap_before_final_norm=False,
        init_cfg=dict(
            type='Pretrained', checkpoint=checkpoint_file,
            prefix='backbone.')),
    neck=dict(in_channels=[96, 192, 384, 768]),
    roi_head=dict(bbox_head=[
        dict(
            type='ConvFCBBoxHead',
            num_shared_convs=4,
            num_shared_fcs=1,
            in_channels=256,
            conv_out_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=80,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0., 0., 0., 0.],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            reg_decoded_bbox=True,
            norm_cfg=dict(type='SyncBN', requires_grad=True),
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
        dict(
            type='ConvFCBBoxHead',
            num_shared_convs=4,
            num_shared_fcs=1,
            in_channels=256,
            conv_out_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=80,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0., 0., 0., 0.],
                target_stds=[0.05, 0.05, 0.1, 0.1]),
            reg_class_agnostic=False,
            reg_decoded_bbox=True,
            norm_cfg=dict(type='SyncBN', requires_grad=True),
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
        dict(
            type='ConvFCBBoxHead',
            num_shared_convs=4,
            num_shared_fcs=1,
            in_channels=256,
            conv_out_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=80,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0., 0., 0., 0.],
                target_stds=[0.033, 0.033, 0.067, 0.067]),
            reg_class_agnostic=False,
            reg_decoded_bbox=True,
            norm_cfg=dict(type='SyncBN', requires_grad=True),
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
    ]))

# augmentation strategy originates from DETR / Sparse RCNN
train_pipeline = [
    dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=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)],
                keep_ratio=True)
        ],
                    [
                        dict(
                            type='RandomChoiceResize',
                            scales=[(400, 1333), (500, 1333), (600, 1333)],
                            keep_ratio=True),
                        dict(
                            type='RandomCrop',
                            crop_type='absolute_range',
                            crop_size=(384, 600),
                            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)],
                            keep_ratio=True)
                    ]]),
    dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))

max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)

# learning rate
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
        end=1000),
    dict(
        type='MultiStepLR',
        begin=0,
        end=max_epochs,
        by_epoch=True,
        milestones=[27, 33],
        gamma=0.1)
]

# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(
    type='AmpOptimWrapper',
    constructor='LearningRateDecayOptimizerConstructor',
    paramwise_cfg={
        'decay_rate': 0.7,
        'decay_type': 'layer_wise',
        'num_layers': 6
    },
    optimizer=dict(
        _delete_=True,
        type='AdamW',
        lr=0.0002,
        betas=(0.9, 0.999),
        weight_decay=0.05))