35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
from ..common.optim import SGD as optimizer
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from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
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from ..common.data.coco import dataloader
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from ..common.models.mask_rcnn_fpn import model
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from ..common.train import train
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from detectron2.config import LazyCall as L
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from detectron2.modeling.backbone import RegNet
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from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
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# Replace default ResNet with RegNetX-4GF from the DDS paper. Config source:
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# https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnetx/RegNetX-4.0GF_dds_8gpu.yaml#L4-L9 # noqa
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model.backbone.bottom_up = L(RegNet)(
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stem_class=SimpleStem,
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stem_width=32,
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block_class=ResBottleneckBlock,
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depth=23,
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w_a=38.65,
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w_0=96,
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w_m=2.43,
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group_width=40,
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freeze_at=2,
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norm="FrozenBN",
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out_features=["s1", "s2", "s3", "s4"],
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)
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model.pixel_std = [57.375, 57.120, 58.395]
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optimizer.weight_decay = 5e-5
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train.init_checkpoint = (
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"https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906383/RegNetX-4.0GF_dds_8gpu.pyth"
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)
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# RegNets benefit from enabling cudnn benchmark mode
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train.cudnn_benchmark = True
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