mask_rcnn_regnety_4gf_dds_fpn_1x.py

from ..common.optim import SGD as optimizer
from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
from ..common.data.coco import dataloader
from ..common.models.mask_rcnn_fpn import model
from ..common.train import train

from detectron2.config import LazyCall as L
from detectron2.modeling.backbone import RegNet
from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock


# Replace default ResNet with RegNetY-4GF from the DDS paper. Config source:
# https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnety/RegNetY-4.0GF_dds_8gpu.yaml#L4-L10  # 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,
    freeze_at=2,
    norm="FrozenBN",
    out_features=["s1", "s2", "s3", "s4"],
)
model.pixel_std = [57.375, 57.120, 58.395]

optimizer.weight_decay = 5e-5
train.init_checkpoint = (
    "https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906838/RegNetY-4.0GF_dds_8gpu.pyth"
)
# RegNets benefit from enabling cudnn benchmark mode
train.cudnn_benchmark = True