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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. | |
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 . import fpn as fpn_module | |
from .pan import PAN | |
from .msr import MSR | |
from . import resnet | |
def build_resnet_backbone(cfg): | |
body = resnet.ResNet(cfg) | |
model = nn.Sequential(OrderedDict([("body", body)])) | |
model.out_channels = cfg.MODEL.RESNETS.BACKBONE_OUT_CHANNELS | |
return model | |
def build_resnet_fpn_backbone(cfg): | |
in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS # 256 | |
in_channels_list = [ | |
in_channels_stage2, # 256 | |
in_channels_stage2 * 2, # 512 | |
in_channels_stage2 * 4, # 1024 | |
in_channels_stage2 * 8, # 2048 | |
] | |
body = resnet.ResNet(cfg) | |
out_channels = cfg.MODEL.RESNETS.BACKBONE_OUT_CHANNELS # 256 | |
fpn = fpn_module.FPN( | |
in_channels_list=in_channels_list, | |
out_channels=out_channels, | |
conv_block=conv_with_kaiming_uniform( | |
cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU, | |
cfg.MODEL.FPN.USE_DEFORMABLE | |
), | |
top_blocks=fpn_module.LastLevelMaxPool(), | |
) | |
if cfg.MODEL.MSR_ON: | |
model = MSR(body, in_channels_list, fpn=fpn) | |
else: | |
model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) | |
model.out_channels = out_channels | |
return model | |
def build_resnet_fpn_backbone(cfg): | |
in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS | |
in_channels_list = [ | |
in_channels_stage2, | |
in_channels_stage2 * 2, | |
in_channels_stage2 * 4, | |
in_channels_stage2 * 8, | |
] | |
body = resnet.ResNet(cfg) | |
out_channels = cfg.MODEL.RESNETS.BACKBONE_OUT_CHANNELS | |
fpn = fpn_module.FPN( | |
in_channels_list=in_channels_list, | |
out_channels=out_channels, | |
conv_block=conv_with_kaiming_uniform( | |
cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU, | |
cfg.MODEL.FPN.USE_DEFORMABLE | |
), | |
top_blocks=fpn_module.LastLevelMaxPool(), | |
) | |
pan = PAN() | |
if cfg.MODEL.MSR_ON: | |
model = MSR(body, in_channels_list, fpn=fpn, pan=pan) | |
else: | |
model = nn.Sequential(OrderedDict([("body", body), | |
("pan", pan), | |
("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 | |
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) | |