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from detectron2.config import LazyCall as L |
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from detectron2.layers import ShapeSpec |
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from detectron2.modeling.meta_arch import RetinaNet |
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from detectron2.modeling.anchor_generator import DefaultAnchorGenerator |
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from detectron2.modeling.backbone.fpn import LastLevelP6P7 |
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from detectron2.modeling.backbone import BasicStem, FPN, ResNet |
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from detectron2.modeling.box_regression import Box2BoxTransform |
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from detectron2.modeling.matcher import Matcher |
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from detectron2.modeling.meta_arch.retinanet import RetinaNetHead |
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from ..data.constants import constants |
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model = L(RetinaNet)( |
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backbone=L(FPN)( |
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bottom_up=L(ResNet)( |
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stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"), |
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stages=L(ResNet.make_default_stages)( |
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depth=50, |
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stride_in_1x1=True, |
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norm="FrozenBN", |
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), |
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out_features=["res3", "res4", "res5"], |
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), |
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in_features=["res3", "res4", "res5"], |
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out_channels=256, |
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top_block=L(LastLevelP6P7)(in_channels=2048, out_channels="${..out_channels}"), |
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), |
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head=L(RetinaNetHead)( |
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input_shape=[ShapeSpec(channels=256)] * 5, |
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num_classes="${..num_classes}", |
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conv_dims=[256, 256, 256, 256], |
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prior_prob=0.01, |
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num_anchors=9, |
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), |
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anchor_generator=L(DefaultAnchorGenerator)( |
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sizes=[[x, x * 2 ** (1.0 / 3), x * 2 ** (2.0 / 3)] for x in [32, 64, 128, 256, 512]], |
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aspect_ratios=[0.5, 1.0, 2.0], |
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strides=[8, 16, 32, 64, 128], |
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offset=0.0, |
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), |
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box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]), |
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anchor_matcher=L(Matcher)( |
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thresholds=[0.4, 0.5], labels=[0, -1, 1], allow_low_quality_matches=True |
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), |
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num_classes=80, |
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head_in_features=["p3", "p4", "p5", "p6", "p7"], |
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focal_loss_alpha=0.25, |
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focal_loss_gamma=2.0, |
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pixel_mean=constants.imagenet_bgr256_mean, |
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pixel_std=constants.imagenet_bgr256_std, |
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input_format="BGR", |
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) |
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