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from .mask_rcnn_R_50_FPN_100ep_LSJ import ( | |
dataloader, | |
lr_multiplier, | |
model, | |
optimizer, | |
train, | |
) | |
from detectron2.config import LazyCall as L | |
from detectron2.modeling.backbone import RegNet | |
from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock | |
# Config source: | |
# https://github.com/facebookresearch/detectron2/blob/main/configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py # noqa | |
model.backbone.bottom_up = L(RegNet)( | |
stem_class=SimpleStem, | |
stem_width=32, | |
block_class=ResBottleneckBlock, | |
depth=22, | |
w_a=31.41, | |
w_0=96, | |
w_m=2.24, | |
group_width=64, | |
se_ratio=0.25, | |
norm="SyncBN", | |
out_features=["s1", "s2", "s3", "s4"], | |
) | |
model.pixel_std = [57.375, 57.120, 58.395] | |
# RegNets benefit from enabling cudnn benchmark mode | |
train.cudnn_benchmark = True | |