transfiner / configs /common /models /mask_rcnn_c4.py
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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",
)