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.fpn import LastLevelMaxPool from detectron2.modeling.backbone import BasicStem, FPN, 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 ( StandardROIHeads, FastRCNNOutputLayers, MaskRCNNConvUpsampleHead, FastRCNNConvFCHead, ) from ..data.constants import constants model = L(GeneralizedRCNN)( 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=["res2", "res3", "res4", "res5"], ), in_features="${.bottom_up.out_features}", out_channels=256, top_block=L(LastLevelMaxPool)(), ), proposal_generator=L(RPN)( in_features=["p2", "p3", "p4", "p5", "p6"], head=L(StandardRPNHead)(in_channels=256, num_anchors=3), anchor_generator=L(DefaultAnchorGenerator)( sizes=[[32], [64], [128], [256], [512]], aspect_ratios=[0.5, 1.0, 2.0], strides=[4, 8, 16, 32, 64], offset=0.0, ), anchor_matcher=L(Matcher)( thresholds=[0.3, 0.7], labels=[0, -1, 1], allow_low_quality_matches=True ), box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]), batch_size_per_image=256, positive_fraction=0.5, pre_nms_topk=(2000, 1000), post_nms_topk=(1000, 1000), nms_thresh=0.7, ), roi_heads=L(StandardROIHeads)( num_classes=80, batch_size_per_image=512, positive_fraction=0.25, proposal_matcher=L(Matcher)( thresholds=[0.5], labels=[0, 1], allow_low_quality_matches=False ), box_in_features=["p2", "p3", "p4", "p5"], box_pooler=L(ROIPooler)( output_size=7, scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32), sampling_ratio=0, pooler_type="ROIAlignV2", ), box_head=L(FastRCNNConvFCHead)( input_shape=ShapeSpec(channels=256, height=7, width=7), conv_dims=[], fc_dims=[1024, 1024], ), box_predictor=L(FastRCNNOutputLayers)( input_shape=ShapeSpec(channels=1024), test_score_thresh=0.05, box2box_transform=L(Box2BoxTransform)(weights=(10, 10, 5, 5)), num_classes="${..num_classes}", ), mask_in_features=["p2", "p3", "p4", "p5"], mask_pooler=L(ROIPooler)( output_size=14, scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32), sampling_ratio=0, pooler_type="ROIAlignV2", ), mask_head=L(MaskRCNNConvUpsampleHead)( input_shape=ShapeSpec(channels=256, width=14, height=14), num_classes="${..num_classes}", conv_dims=[256, 256, 256, 256, 256], ), ), pixel_mean=constants.imagenet_bgr256_mean, pixel_std=constants.imagenet_bgr256_std, input_format="BGR", )