detnas_supernet_frcnn_shufflenetv2_fpn_1x_coco.py

_base_ = [
    '../../_base_/datasets/mmdet/coco_detection.py',
    '../../_base_/schedules/mmdet/schedule_1x.py',
    '../../_base_/mmdet_runtime.py'
]

norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='mmdet.FasterRCNN',
    backbone=dict(
        type='mmcls.SearchableShuffleNetV2',
        norm_cfg=norm_cfg,
        out_indices=(0, 1, 2, 3),
        widen_factor=1.0,
        with_last_layer=False),
    neck=dict(
        type='FPN',
        norm_cfg=norm_cfg,
        in_channels=[64, 160, 320, 640],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared4Conv1FCBBoxHead',
            norm_cfg=norm_cfg,
            in_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,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=False,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100)
        # soft-nms is also supported for rcnn testing
        # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
    ),
)

mutator = dict(
    type='OneShotMutator',
    placeholder_mapping=dict(
        all_blocks=dict(
            type='OneShotOP',
            choices=dict(
                shuffle_3x3=dict(
                    type='ShuffleBlock', norm_cfg=norm_cfg, kernel_size=3),
                shuffle_5x5=dict(
                    type='ShuffleBlock', norm_cfg=norm_cfg, kernel_size=5),
                shuffle_7x7=dict(
                    type='ShuffleBlock', norm_cfg=norm_cfg, kernel_size=7),
                shuffle_xception=dict(
                    type='ShuffleXception',
                    norm_cfg=norm_cfg,
                ),
            ))))

algorithm = dict(
    type='DetNAS',
    architecture=dict(
        type='MMDetArchitecture',
        model=model,
    ),
    mutator=mutator,
    pruner=None,
    distiller=None,
    retraining=False,
)

find_unused_parameters = True