# 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 @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)])) model.out_channels = cfg.MODEL.RESNETS.BACKBONE_OUT_CHANNELS return model @registry.BACKBONES.register("R-50-FPN") @registry.BACKBONES.register("R-101-FPN") @registry.BACKBONES.register("R-152-FPN") 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 @registry.BACKBONES.register("R-50-PAN") @registry.BACKBONES.register("R-101-PAN") @registry.BACKBONES.register("R-152-PAN") 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 @registry.BACKBONES.register("R-50-FPN-RETINANET") @registry.BACKBONES.register("R-101-FPN-RETINANET") 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)