_base_ = '../mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-1x_coco.py'
# model settings
model = dict(
    type='PointRend',
    roi_head=dict(
        type='PointRendRoIHead',
        mask_roi_extractor=dict(
            type='GenericRoIExtractor',
            aggregation='concat',
            roi_layer=dict(
                _delete_=True, type='SimpleRoIAlign', output_size=14),
            out_channels=256,
            featmap_strides=[4]),
        mask_head=dict(
            _delete_=True,
            type='CoarseMaskHead',
            num_fcs=2,
            in_channels=256,
            conv_out_channels=256,
            fc_out_channels=1024,
            num_classes=80,
            loss_mask=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
        point_head=dict(
            type='MaskPointHead',
            num_fcs=3,
            in_channels=256,
            fc_channels=256,
            num_classes=80,
            coarse_pred_each_layer=True,
            loss_point=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
    # model training and testing settings
    train_cfg=dict(
        rcnn=dict(
            mask_size=7,
            num_points=14 * 14,
            oversample_ratio=3,
            importance_sample_ratio=0.75)),
    test_cfg=dict(
        rcnn=dict(
            subdivision_steps=5,
            subdivision_num_points=28 * 28,
            scale_factor=2)))

_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# model settings
model = dict(
    type='PointRend',
    roi_head=dict(
        type='PointRendRoIHead',
        mask_roi_extractor=dict(
            type='GenericRoIExtractor',
            aggregation='concat',
            roi_layer=dict(
                _delete_=True, type='SimpleRoIAlign', output_size=14),
            out_channels=256,
            featmap_strides=[4]),
        mask_head=dict(
            _delete_=True,
            type='CoarseMaskHead',
            num_fcs=2,
            in_channels=256,
            conv_out_channels=256,
            fc_out_channels=1024,
            num_classes=80,
            loss_mask=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
        point_head=dict(
            type='MaskPointHead',
            num_fcs=3,
            in_channels=256,
            fc_channels=256,
            num_classes=80,
            coarse_pred_each_layer=True,
            loss_point=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
    # model training and testing settings
    train_cfg=dict(
        rcnn=dict(
            mask_size=7,
            num_points=14 * 14,
            oversample_ratio=3,
            importance_sample_ratio=0.75)),
    test_cfg=dict(
        rcnn=dict(
            subdivision_steps=5,
            subdivision_num_points=28 * 28,
            scale_factor=2)))
