configs/common/models/mask_rcnn_c4.py

from detectron2.config import LazyCall as L
from detectron2.layers import ShapeSpec
from detectron2.modeling.meta_arch import GeneralizedRCNN
from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
from detectron2.modeling.backbone import BasicStem, BottleneckBlock, ResNet
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.matcher import Matcher
from detectron2.modeling.poolers import ROIPooler
from detectron2.modeling.proposal_generator import RPN, StandardRPNHead
from detectron2.modeling.roi_heads import (
    FastRCNNOutputLayers,
    MaskRCNNConvUpsampleHead,
    Res5ROIHeads,
)

model = L(GeneralizedRCNN)(
    backbone=L(ResNet)(
        stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"),
        stages=L(ResNet.make_default_stages)(
            depth=50,
            stride_in_1x1=True,
            norm="FrozenBN",
        ),
        out_features=["res4"],
    ),
    proposal_generator=L(RPN)(
        in_features=["res4"],
        head=L(StandardRPNHead)(in_channels=1024, num_anchors=15),
        anchor_generator=L(DefaultAnchorGenerator)(
            sizes=[[32, 64, 128, 256, 512]],
            aspect_ratios=[0.5, 1.0, 2.0],
            strides=[16],
            offset=0.0,
        ),
        anchor_matcher=L(Matcher)(
            thresholds=[0.3, 0.7], labels=[0, -1, 1], allow_low_quality_matches=True
        ),
        box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]),
        batch_size_per_image=256,
        positive_fraction=0.5,
        pre_nms_topk=(12000, 6000),
        post_nms_topk=(2000, 1000),
        nms_thresh=0.7,
    ),
    roi_heads=L(Res5ROIHeads)(
        num_classes=80,
        batch_size_per_image=512,
        positive_fraction=0.25,
        proposal_matcher=L(Matcher)(
            thresholds=[0.5], labels=[0, 1], allow_low_quality_matches=False
        ),
        in_features=["res4"],
        pooler=L(ROIPooler)(
            output_size=14,
            scales=(1.0 / 16,),
            sampling_ratio=0,
            pooler_type="ROIAlignV2",
        ),
        res5=L(ResNet.make_stage)(
            block_class=BottleneckBlock,
            num_blocks=3,
            stride_per_block=[2, 1, 1],
            in_channels=1024,
            bottleneck_channels=512,
            out_channels=2048,
            norm="FrozenBN",
            stride_in_1x1=True,
        ),
        box_predictor=L(FastRCNNOutputLayers)(
            input_shape=L(ShapeSpec)(channels="${...res5.out_channels}", height=1, width=1),
            test_score_thresh=0.05,
            box2box_transform=L(Box2BoxTransform)(weights=(10, 10, 5, 5)),
            num_classes="${..num_classes}",
        ),
        mask_head=L(MaskRCNNConvUpsampleHead)(
            input_shape=L(ShapeSpec)(
                channels="${...res5.out_channels}",
                width="${...pooler.output_size}",
                height="${...pooler.output_size}",
            ),
            num_classes="${..num_classes}",
            conv_dims=[256],
        ),
    ),
    pixel_mean=[103.530, 116.280, 123.675],
    pixel_std=[1.0, 1.0, 1.0],
    input_format="BGR",
)