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Python make_layers.conv_with_kaiming_uniform方法代码示例

本文整理汇总了Python中maskrcnn_benchmark.modeling.make_layers.conv_with_kaiming_uniform方法的典型用法代码示例。如果您正苦于以下问题：Python make_layers.conv_with_kaiming_uniform方法的具体用法？Python make_layers.conv_with_kaiming_uniform怎么用？Python make_layers.conv_with_kaiming_uniform使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类maskrcnn_benchmark.modeling.make_layers的用法示例。


在下文中一共展示了make_layers.conv_with_kaiming_uniform方法的13个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: build_res2net_fpn_backbone

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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
开发者ID:Res2Net，项目名称:Res2Net-maskrcnn，代码行数:22，代码来源:res2net_builder.py

示例2: build_resnet_fpn_backbone

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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
开发者ID:Res2Net，项目名称:Res2Net-maskrcnn，代码行数:22，代码来源:backbone.py

示例3: build_resnet_fpn_p3p7_backbone

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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
开发者ID:Res2Net，项目名称:Res2Net-maskrcnn，代码行数:24，代码来源:backbone.py

示例4: build_detnasnet_fpn_backbone

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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
开发者ID:megvii-model，项目名称:DetNAS，代码行数:27，代码来源:backbone.py

示例5: build_detnasnet_fpn_p3p7_backbone

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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
开发者ID:megvii-model，项目名称:DetNAS，代码行数:18，代码来源:backbone.py

示例6: build_resnet_fpn_backbone

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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
开发者ID:clw5180，项目名称:remote_sensing_object_detection_2019，代码行数:21，代码来源:backbone.py

示例7: build_resnet_fpn_p4p7_backbone

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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
开发者ID:ChenJoya，项目名称:sampling-free，代码行数:24，代码来源:backbone.py

示例8: build_resnet_fpn_p3p7_backbone

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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
开发者ID:mlperf，项目名称:training，代码行数:23，代码来源:backbone.py

示例9: build_mnv2_fpn_backbone

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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
开发者ID:Lausannen，项目名称:NAS-FCOS，代码行数:27，代码来源:backbone.py

示例10: build_resnet_fpn_backbone

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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
开发者ID:ucas-vg，项目名称:TinyBenchmark，代码行数:24，代码来源:backbone.py

示例11: build_resnet_fpn_p3p7_backbone

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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
开发者ID:ucas-vg，项目名称:TinyBenchmark，代码行数:26，代码来源:backbone.py

示例12: build_res2net_fpn_p3p7_backbone

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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)
开发者ID:Res2Net，项目名称:Res2Net-maskrcnn，代码行数:32，代码来源:res2net_builder.py

示例13: add_conv_body_fpn

# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform [as 别名]
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
开发者ID:Lausannen，项目名称:NAS-FCOS，代码行数:29，代码来源:fbnet.py


注：本文中的maskrcnn_benchmark.modeling.make_layers.conv_with_kaiming_uniform方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。