|
|
|
|
|
from detectron2.config import LazyCall as L
|
|
from detectron2.layers import ShapeSpec
|
|
from detectron2.modeling.meta_arch import RetinaNet
|
|
from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
|
|
from detectron2.modeling.backbone.fpn import LastLevelP6P7
|
|
from detectron2.modeling.backbone import BasicStem, FPN, ResNet
|
|
from detectron2.modeling.box_regression import Box2BoxTransform
|
|
from detectron2.modeling.matcher import Matcher
|
|
from detectron2.modeling.meta_arch.retinanet import RetinaNetHead
|
|
|
|
from ..data.constants import constants
|
|
|
|
model = L(RetinaNet)(
|
|
backbone=L(FPN)(
|
|
bottom_up=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=["res3", "res4", "res5"],
|
|
),
|
|
in_features=["res3", "res4", "res5"],
|
|
out_channels=256,
|
|
top_block=L(LastLevelP6P7)(in_channels=2048, out_channels="${..out_channels}"),
|
|
),
|
|
head=L(RetinaNetHead)(
|
|
|
|
input_shape=[ShapeSpec(channels=256)] * 5,
|
|
num_classes="${..num_classes}",
|
|
conv_dims=[256, 256, 256, 256],
|
|
prior_prob=0.01,
|
|
num_anchors=9,
|
|
),
|
|
anchor_generator=L(DefaultAnchorGenerator)(
|
|
sizes=[[x, x * 2 ** (1.0 / 3), x * 2 ** (2.0 / 3)] for x in [32, 64, 128, 256, 512]],
|
|
aspect_ratios=[0.5, 1.0, 2.0],
|
|
strides=[8, 16, 32, 64, 128],
|
|
offset=0.0,
|
|
),
|
|
box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]),
|
|
anchor_matcher=L(Matcher)(
|
|
thresholds=[0.4, 0.5], labels=[0, -1, 1], allow_low_quality_matches=True
|
|
),
|
|
num_classes=80,
|
|
head_in_features=["p3", "p4", "p5", "p6", "p7"],
|
|
focal_loss_alpha=0.25,
|
|
focal_loss_gamma=2.0,
|
|
pixel_mean=constants.imagenet_bgr256_mean,
|
|
pixel_std=constants.imagenet_bgr256_std,
|
|
input_format="BGR",
|
|
)
|
|
|
