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from collections import OrderedDict
from torch import nn
from maskrcnn_benchmark.modeling import registry
from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform
from maskrcnn_benchmark.layers import DropBlock2D, DyHead
from . import fpn as fpn_module
from . import bifpn
from . import resnet
from . import efficientnet
from . import efficientdet
from . import swint
from . import swint_v2
from . import swint_vl
from . import swint_v2_vl
@registry.BACKBONES.register("R-50-C4")
@registry.BACKBONES.register("R-50-C5")
@registry.BACKBONES.register("R-101-C4")
@registry.BACKBONES.register("R-101-C5")
def build_resnet_backbone(cfg):
body = resnet.ResNet(cfg)
model = nn.Sequential(OrderedDict([("body", body)]))
return model
@registry.BACKBONES.register("R-50-RETINANET")
@registry.BACKBONES.register("R-101-RETINANET")
def build_resnet_c5_backbone(cfg):
body = resnet.ResNet(cfg)
model = nn.Sequential(OrderedDict([("body", body)]))
return model
@registry.BACKBONES.register("SWINT-FPN-RETINANET")
def build_retinanet_swint_fpn_backbone(cfg):
"""
Args:
cfg: a detectron2 CfgNode
Returns:
backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
"""
if cfg.MODEL.SWINT.VERSION == "v1":
body = swint.build_swint_backbone(cfg)
elif cfg.MODEL.SWINT.VERSION == "v2":
body = swint_v2.build_swint_backbone(cfg)
elif cfg.MODEL.SWINT.VERSION == "vl":
body = swint_vl.build_swint_backbone(cfg)
elif cfg.MODEL.SWINT.VERSION == "v2_vl":
body = swint_v2_vl.build_swint_backbone(cfg)
in_channels_stages = cfg.MODEL.SWINT.OUT_CHANNELS
out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
in_channels_p6p7 = out_channels
fpn = fpn_module.FPN(
in_channels_list=[
0,
in_channels_stages[-3],
in_channels_stages[-2],
in_channels_stages[-1],
],
out_channels=out_channels,
conv_block=conv_with_kaiming_uniform(
cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU
),
top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels),
drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None,
use_spp=cfg.MODEL.FPN.USE_SPP,
use_pan=cfg.MODEL.FPN.USE_PAN,
return_swint_feature_before_fusion=cfg.MODEL.FPN.RETURN_SWINT_FEATURE_BEFORE_FUSION
)
if cfg.MODEL.FPN.USE_DYHEAD:
dyhead = DyHead(cfg, out_channels)
model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn), ("dyhead", dyhead)]))
else:
model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
return model
@registry.BACKBONES.register("SWINT-FPN")
def build_swint_fpn_backbone(cfg):
"""
Args:
cfg: a detectron2 CfgNode
Returns:
backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
"""
if cfg.MODEL.SWINT.VERSION == "v1":
body = swint.build_swint_backbone(cfg)
elif cfg.MODEL.SWINT.VERSION == "v2":
body = swint_v2.build_swint_backbone(cfg)
elif cfg.MODEL.SWINT.VERSION == "vl":
body = swint_vl.build_swint_backbone(cfg)
elif cfg.MODEL.SWINT.VERSION == "v2_vl":
body = swint_v2_vl.build_swint_backbone(cfg)
in_channels_stages = cfg.MODEL.SWINT.OUT_CHANNELS
out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
fpn = fpn_module.FPN(
in_channels_list=[
in_channels_stages[-4],
in_channels_stages[-3],
in_channels_stages[-2],
in_channels_stages[-1],
],
out_channels=out_channels,
conv_block=conv_with_kaiming_uniform(
cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU
),
top_blocks=fpn_module.LastLevelMaxPool(),
drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None,
use_spp=cfg.MODEL.FPN.USE_SPP,
use_pan=cfg.MODEL.FPN.USE_PAN
)
if cfg.MODEL.FPN.USE_DYHEAD:
dyhead = DyHead(cfg, out_channels)
model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn), ("dyhead", dyhead)]))
else:
model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
return model
@registry.BACKBONES.register("CVT-FPN-RETINANET")
def build_retinanet_cvt_fpn_backbone(cfg):
"""
Args:
cfg: a detectron2 CfgNode
Returns:
backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
"""
body = cvt.build_cvt_backbone(cfg)
in_channels_stages = cfg.MODEL.SPEC.DIM_EMBED
out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
in_channels_p6p7 = out_channels
fpn = fpn_module.FPN(
in_channels_list=[
0,
in_channels_stages[-3],
in_channels_stages[-2],
in_channels_stages[-1],
],
out_channels=out_channels,
conv_block=conv_with_kaiming_uniform(
cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU
),
top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels),
drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None,
use_spp=cfg.MODEL.FPN.USE_SPP,
use_pan=cfg.MODEL.FPN.USE_PAN
)
if cfg.MODEL.FPN.USE_DYHEAD:
dyhead = DyHead(cfg, out_channels)
model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn), ("dyhead", dyhead)]))
else:
model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
return model
@registry.BACKBONES.register("EFFICIENT7-FPN-RETINANET")
@registry.BACKBONES.register("EFFICIENT7-FPN-FCOS")
@registry.BACKBONES.register("EFFICIENT5-FPN-RETINANET")
@registry.BACKBONES.register("EFFICIENT5-FPN-FCOS")
@registry.BACKBONES.register("EFFICIENT3-FPN-RETINANET")
@registry.BACKBONES.register("EFFICIENT3-FPN-FCOS")
def build_eff_fpn_p6p7_backbone(cfg):
version = cfg.MODEL.BACKBONE.CONV_BODY.split('-')[0]
version = version.replace('EFFICIENT', 'b')
body = efficientnet.get_efficientnet(cfg, version)
in_channels_stage = body.out_channels
out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
in_channels_p6p7 = out_channels
in_channels_stage[0] = 0
fpn = fpn_module.FPN(
in_channels_list=in_channels_stage,
out_channels=out_channels,
conv_block=conv_with_kaiming_uniform(
cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU
),
top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels),
drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None,
use_spp=cfg.MODEL.FPN.USE_SPP,
use_pan=cfg.MODEL.FPN.USE_PAN
)
model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
return model
@registry.BACKBONES.register("EFFICIENT7-BIFPN-RETINANET")
@registry.BACKBONES.register("EFFICIENT7-BIFPN-FCOS")
@registry.BACKBONES.register("EFFICIENT5-BIFPN-RETINANET")
@registry.BACKBONES.register("EFFICIENT5-BIFPN-FCOS")
@registry.BACKBONES.register("EFFICIENT3-BIFPN-RETINANET")
@registry.BACKBONES.register("EFFICIENT3-BIFPN-FCOS")
def build_eff_fpn_p6p7_backbone(cfg):
version = cfg.MODEL.BACKBONE.CONV_BODY.split('-')[0]
version = version.replace('EFFICIENT', 'b')
body = efficientnet.get_efficientnet(cfg, version)
in_channels_stage = body.out_channels
out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
bifpns = nn.ModuleList()
for i in range(cfg.MODEL.BIFPN.NUM_REPEATS):
first_time = (i==0)
fpn = bifpn.BiFPN(
in_channels_list=in_channels_stage[1:],
out_channels=out_channels,
first_time=first_time,
attention=cfg.MODEL.BIFPN.USE_ATTENTION
)
bifpns.append(fpn)
model = nn.Sequential(OrderedDict([("body", body), ("bifpn", bifpns)]))
return model
@registry.BACKBONES.register("EFFICIENT-DET")
def build_efficientdet_backbone(cfg):
efficientdet.g_simple_padding = True
compound = cfg.MODEL.BACKBONE.EFFICIENT_DET_COMPOUND
start_from = cfg.MODEL.BACKBONE.EFFICIENT_DET_START_FROM
model = efficientdet.EffNetFPN(
compound_coef=compound,
start_from=start_from,
)
if cfg.MODEL.BACKBONE.USE_SYNCBN:
import torch
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
return model
def build_backbone(cfg):
assert cfg.MODEL.BACKBONE.CONV_BODY in registry.BACKBONES, \
"cfg.MODEL.BACKBONE.CONV_BODY: {} are not registered in registry".format(
cfg.MODEL.BACKBONE.CONV_BODY
)
return registry.BACKBONES[cfg.MODEL.BACKBONE.CONV_BODY](cfg)
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