detection

conv_cfg = dict(type="ConvWS")
norm_cfg = dict(type="GN", num_groups=32, requires_grad=True)
model = dict(
    type="FasterRCNN",
    pretrained="open-mmlab://jhu/resnet50_gn_ws",
    backbone=dict(
        type="ResNet",
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        conv_cfg=conv_cfg,
        norm_cfg=norm_cfg,
        norm_eval=True,
        style="pytorch",
    ),
    neck=dict(
        type="FPN",
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        conv_cfg=conv_cfg,
        norm_cfg=norm_cfg,
        num_outs=5,
    ),
    rpn_head=dict(
        type="RPNHead",
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type="AnchorGenerator",
            ratios=[0.5, 1.0, 2.0],
            scales=[8],
            strides=[4, 8, 16, 32, 64],
        ),
        bbox_coder=dict(
            type="DeltaXYWHBBoxCoder",
            target_means=[0.0, 0.0, 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", out_size=7, sample_num=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32],
        ),
        bbox_head=dict(
            type="Shared4Conv1FCBBoxHead",
            in_channels=256,
            conv_out_channels=256,
            fc_out_channels=1024,
            conv_cfg=conv_cfg,
            norm_cfg=norm_cfg,
            roi_feat_size=7,
            num_classes={{cookiecutter.num_classes}},
            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],
            ),
            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=0,
        pos_weight=-1,
        debug=False,
    ),
    rpn_proposal=dict(
        nms_across_levels=False,
        nms_pre=2000,
        nms_post=1000,
        max_num=1000,
        nms_thr=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_across_levels=False,
        nms_pre=1000,
        nms_post=1000,
        max_num=1000,
        nms_thr=0.7,
        min_bbox_size=0,
    ),
    rcnn=dict(score_thr=0.05, nms=dict(type="nms", iou_thr=0.5), max_per_img=100),
)

load_from = "/media/VA/pretrained_weights/mmdet/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth"