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Running
on
Zero
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", | |
) | |