Python maskrcnn_benchmark.modeling.make_layers.conv_with_kaiming_uniform() Examples

The following are 29 code examples of maskrcnn_benchmark.modeling.make_layers.conv_with_kaiming_uniform(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module maskrcnn_benchmark.modeling.make_layers , or try the search function .
Example #1
Source File: backbone.py    From sampling-free with MIT License 6 votes vote down vote up 
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
Example #2
Source File: backbone.py    From TinyBenchmark with MIT License 6 votes vote down vote up 
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),
        upsample_rates=cfg.MODEL.FPN.UPSAMPLE_RATE,   # add by hui
        upsample_mode=cfg.MODEL.FPN.UPSAMPLE_MODE     # add by hui
    )
    model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
    model.out_channels = out_channels
    return model
Example #3
Source File: backbone.py    From TinyBenchmark with MIT License 6 votes vote down vote up 
def build_resnet_fpn_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.RESNETS.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(),
        upsample_rates=cfg.MODEL.FPN.UPSAMPLE_RATE,  # add by hui
        upsample_mode=cfg.MODEL.FPN.UPSAMPLE_MODE  # add by hui
    )
    model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
    model.out_channels = out_channels
    return model
Example #4
Source File: backbone.py    From DF-Traffic-Sign-Identification with MIT License 6 votes vote down vote up 
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
Example #5
Source File: backbone.py    From DF-Traffic-Sign-Identification with MIT License 6 votes vote down vote up 
def build_resnet_fpn_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.RESNETS.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
Example #6
Source File: backbone.py    From RRPN_pytorch with MIT License 6 votes vote down vote up 
def build_resnet_fpn_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.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)]))
    return model
Example #7
Source File: backbone.py    From NAS-FCOS with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def build_resnet_fpn_p3p7_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.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)]))
    return model
Example #8
Source File: backbone.py    From NAS-FCOS with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def build_resnet_fpn_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.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)]))
    return model
Example #9
Source File: backbone.py    From NAS-FCOS with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def build_mnv2_fpn_backbone(cfg):
    body = mobilenet.MobileNetV2(cfg)
    in_channels_stage2 = cfg.MODEL.BACKBONE.ENCODER_OUT_CHANNELS
    out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
    fpn = fpn_module.FPN(
        in_channels_list=[
            0,
            in_channels_stage2[1],
            in_channels_stage2[2],
            in_channels_stage2[3],
        ],
        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(out_channels, out_channels),
    )
    if cfg.MODEL.BACKBONE.SPLIT:
        # separate backbone and fpn output
        return body, fpn
    else:
        model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
        if cfg.MODEL.PANOPTIC.DECODER != "none":
            return model, None
    return model
Example #10
Source File: backbone.py    From training with Apache License 2.0 6 votes vote down vote up 
def build_resnet_fpn_p3p7_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.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)]))
    return model
Example #11
Source File: backbone.py    From training with Apache License 2.0 6 votes vote down vote up 
def build_resnet_fpn_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.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)]))
    return model
Example #12
Source File: backbone.py    From maskrcnn-benchmark with MIT License 6 votes vote down vote up 
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
Example #13
Source File: backbone.py    From maskrcnn-benchmark with MIT License 6 votes vote down vote up 
def build_resnet_fpn_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.RESNETS.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
Example #14
Source File: backbone.py    From sampling-free with MIT License 6 votes vote down vote up 
def build_resnet_fpn_p4p7_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,
            0,
            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
Example #15
Source File: res2net_builder.py    From Res2Net-maskrcnn with MIT License 6 votes vote down vote up 
def build_res2net_fpn_backbone(cfg):
    body = res2net.Res2Net(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.RESNETS.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
Example #16
Source File: backbone.py    From sampling-free with MIT License 6 votes vote down vote up 
def build_resnet_fpn_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.RESNETS.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
Example #17
Source File: backbone.py    From remote_sensing_object_detection_2019 with MIT License 6 votes vote down vote up 
def build_resnet_fpn_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.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)]))
    return model
Example #18
Source File: backbone.py    From DetNAS with MIT License 6 votes vote down vote up 
def build_detnasnet_fpn_p3p7_backbone(cfg):
    body = detnasnet.ShuffleNetV2DetNAS(cfg)
    out_channels = cfg.MODEL.RESNETS.BACKBONE_OUT_CHANNELS
    fpn = fpn_module.FPN(
        in_channels_list=[0, 160, 320, 640,],
        out_channels=out_channels,
        conv_block=conv_with_kaiming_uniform(
            cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU, cfg.MODEL.FPN.USE_SYNCBN
        ),
        top_blocks=fpn_module.LastLevelP6P7(out_channels, out_channels, cfg.MODEL.RETINANET.P6P7_USE_SYNCBN),
    )
    if 'search' in cfg.MODEL.BACKBONE.CONV_BODY:
        return body, fpn
    model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
    model.out_channels = out_channels
    return model
Example #19
Source File: backbone.py    From DetNAS with MIT License 6 votes vote down vote up 
def build_detnasnet_fpn_backbone(cfg):
    body = detnasnet.ShuffleNetV2DetNAS(cfg)
    out_channels = cfg.MODEL.RESNETS.BACKBONE_OUT_CHANNELS
    if '300M' in cfg.MODEL.BACKBONE.CONV_BODY:
        in_channels_list = [64, 160, 320, 640,]
    elif '1.3G' in cfg.MODEL.BACKBONE.CONV_BODY:
        in_channels_list = [96, 240, 480, 960,]
    elif '3.8G' in cfg.MODEL.BACKBONE.CONV_BODY:
        in_channels_list = [172, 432, 864, 1728,]
    else:
        raise ValueError("Wrong backbone size.")

    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_SYNCBN
        ),
        top_blocks=fpn_module.LastLevelMaxPool(),
    )
    if 'search' in cfg.MODEL.BACKBONE.CONV_BODY:
        return body, fpn
    model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
    model.out_channels = out_channels
    return model
Example #20
Source File: backbone.py    From DetNAS with MIT License 6 votes vote down vote up 
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
Example #21
Source File: backbone.py    From DetNAS with MIT License 6 votes vote down vote up 
def build_resnet_fpn_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.RESNETS.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
Example #22
Source File: backbone.py    From Clothing-Detection with GNU General Public License v3.0 6 votes vote down vote up 
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
Example #23
Source File: backbone.py    From Clothing-Detection with GNU General Public License v3.0 6 votes vote down vote up 
def build_resnet_fpn_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.RESNETS.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
Example #24
Source File: backbone.py    From R2CNN.pytorch with MIT License 6 votes vote down vote up 
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
Example #25
Source File: backbone.py    From R2CNN.pytorch with MIT License 6 votes vote down vote up 
def build_resnet_fpn_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.RESNETS.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
Example #26
Source File: backbone.py    From Res2Net-maskrcnn with MIT License 6 votes vote down vote up 
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
Example #27
Source File: backbone.py    From Res2Net-maskrcnn with MIT License 6 votes vote down vote up 
def build_resnet_fpn_backbone(cfg):
    body = resnet.ResNet(cfg)
    in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
    out_channels = cfg.MODEL.RESNETS.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
Example #28
Source File: fbnet.py    From NAS-FCOS with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def add_conv_body_fpn(cfg, dim_in=3):
    builder, arch_def = create_builder(cfg)

    body = FBNetTrunk(builder, arch_def, dim_in)
    in_channels_stage2 = cfg.MODEL.BACKBONE.ENCODER_OUT_CHANNELS
    out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
    fpn = fpn_module.FPN(
        in_channels_list=[
            0,
            in_channels_stage2[1],
            in_channels_stage2[2],
            in_channels_stage2[3]
        ],
        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(out_channels, out_channels),
    )
    if cfg.MODEL.BACKBONE.SPLIT:
        # separate backbone and fpn output
        return body, fpn
    else:
        model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
        if cfg.MODEL.PANOPTIC.DECODER != "none":
            return model, None
    return model
Example #29
Source File: res2net_builder.py    From Res2Net-maskrcnn with MIT License 5 votes vote down vote up 
def build_res2net_fpn_p3p7_backbone(cfg):
    body = res2net.Res2Net(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)