def build_senet_libra_backbone(cfg):
body = senet.build_senet(cfg)
in_channels_stage2 = cfg.MODEL.SENET.RES2_OUT_CHANNELS
out_channels = cfg.MODEL.SENET.BACKBONE_OUT_CHANNELS
fpn = pan_module.FPN(
in_channels_list=[
in_channels_stage2,
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
],
out_channels=out_channels,
conv_block=conv_with_kaiming_uniform(cfg.MODEL.PAN.USE_GN,
cfg.MODEL.FPN.USE_RELU),
top_blocks=fpn_module.LastLevelMaxPool(),
)
bfp = bfp_module.BFP(
in_channels=out_channels,
num_levels=cfg.MODEL.LIBRA.NUM_LEVELS,
refine_level=cfg.MODEL.LIBRA.REFINE_LEVEL,
refine_type=cfg.MODEL.LIBRA.REFINE_TYPE,
)
model = nn.Sequential(
OrderedDict([("body", body), ("fpn", fpn), ("bfp", bfp)]))
model.out_channels = out_channels
return model
def build_resnet_pan_backbone(cfg):
body = resnet.ResNet(cfg)
in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
out_channels = cfg.MODEL.RESNETS.BACKBONE_OUT_CHANNELS
pan = pan_module.PAN(
in_channels_list=[
in_channels_stage2,
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
],
out_channels=out_channels,
conv_block=conv_with_kaiming_uniform(cfg.MODEL.PAN.USE_GN,
cfg.MODEL.FPN.USE_RELU),
top_blocks=fpn_module.LastLevelMaxPool(),
)
model = nn.Sequential(OrderedDict([("body", body), ("pan", pan)]))
model.out_channels = out_channels
return model
def build_senet_fpn_backbone(cfg):
body = senet.build_senet(cfg)
in_channels_stage2 = cfg.MODEL.SENET.RES2_OUT_CHANNELS
out_channels = cfg.MODEL.SENET.BACKBONE_OUT_CHANNELS
fpn = fpn_module.FPN(
in_channels_list=[
in_channels_stage2,
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
],
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(),
)
model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
model.out_channels = out_channels
return model
def build_resnet_fpn_p3p7_backbone(cfg):
body = resnet.ResNet(cfg)
in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
out_channels = cfg.MODEL.RESNETS.BACKBONE_OUT_CHANNELS
in_channels_p6p7 = in_channels_stage2 * 8 if cfg.MODEL.RETINANET.USE_C5 \
else out_channels
fpn = fpn_module.FPN(
in_channels_list=[
0,
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
],
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),
)
model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
model.out_channels = out_channels
return model