Python poolers.Pooler方法代码示例

本文整理汇总了Python中maskrcnn_benchmark.modeling.poolers.Pooler方法的典型用法代码示例。如果您正苦于以下问题：Python poolers.Pooler方法的具体用法？Python poolers.Pooler怎么用？Python poolers.Pooler使用的例子？那么恭喜您, 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.poolers的用法示例。

在下文中一共展示了poolers.Pooler方法的7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: init

# 需要导入模块: from maskrcnn_benchmark.modeling import poolers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.poolers import Pooler [as 别名]
def __init__(self, cfg, in_channels):
        super(FPN2MLPFeatureExtractor, self).__init__()

        resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
        scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES
        sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
        pooler = Pooler(
            output_size=(resolution, resolution),
            scales=scales,
            sampling_ratio=sampling_ratio,
        )
        input_size = in_channels * resolution ** 2
        representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
        use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN
        self.pooler = pooler
        self.fc6 = make_fc(input_size, representation_size, use_gn)
        self.fc7 = make_fc(representation_size, representation_size, use_gn)
        self.out_channels = representation_size

开发者ID:Res2Net，项目名称:Res2Net-maskrcnn，代码行数:20，代码来源:roi_box_feature_extractors.py

示例2: init

# 需要导入模块: from maskrcnn_benchmark.modeling import poolers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.poolers import Pooler [as 别名]
def __init__(self, cfg):
        super(FPN2MLPFeatureExtractor, self).__init__()

        resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
        scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES
        sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
        pooler = Pooler(
            output_size=(resolution, resolution),
            scales=scales,
            sampling_ratio=sampling_ratio,
        )
        input_size = cfg.MODEL.BACKBONE.OUT_CHANNELS * resolution ** 2
        representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
        use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN
        self.pooler = pooler
        self.fc6 = make_fc(input_size, representation_size, use_gn)
        self.fc7 = make_fc(representation_size, representation_size, use_gn)

开发者ID:clw5180，项目名称:remote_sensing_object_detection_2019，代码行数:19，代码来源:roi_box_feature_extractors.py

示例3: init

# 需要导入模块: from maskrcnn_benchmark.modeling import poolers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.poolers import Pooler [as 别名]
def __init__(self, config):
        super(ResNet50Conv5ROIFeatureExtractor, self).__init__()

        resolution = config.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
        scales = config.MODEL.ROI_BOX_HEAD.POOLER_SCALES
        sampling_ratio = config.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
        pooler = Pooler(
            output_size=(resolution, resolution),
            scales=scales,
            sampling_ratio=sampling_ratio,
        )

        stage = resnet.StageSpec(index=4, block_count=3, return_features=False)
        head = resnet.ResNetHead(
            block_module=config.MODEL.RESNETS.TRANS_FUNC,
            stages=(stage,),
            num_groups=config.MODEL.RESNETS.NUM_GROUPS,
            width_per_group=config.MODEL.RESNETS.WIDTH_PER_GROUP,
            stride_in_1x1=config.MODEL.RESNETS.STRIDE_IN_1X1,
            stride_init=None,
            res2_out_channels=config.MODEL.RESNETS.RES2_OUT_CHANNELS,
        )

        self.pooler = pooler
        self.head = head

开发者ID:HRNet，项目名称:HRNet-MaskRCNN-Benchmark，代码行数:27，代码来源:roi_box_feature_extractors.py

示例4: init

# 需要导入模块: from maskrcnn_benchmark.modeling import poolers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.poolers import Pooler [as 别名]
def __init__(self, cfg):
        super(KeypointRCNNFeatureExtractor, self).__init__()

        resolution = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION
        scales = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SCALES
        sampling_ratio = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO
        pooler = Pooler(
            output_size=(resolution, resolution),
            scales=scales,
            sampling_ratio=sampling_ratio,
        )
        self.pooler = pooler

        input_features = cfg.MODEL.BACKBONE.OUT_CHANNELS
        layers = cfg.MODEL.ROI_KEYPOINT_HEAD.CONV_LAYERS
        next_feature = input_features
        self.blocks = []
        for layer_idx, layer_features in enumerate(layers, 1):
            layer_name = "conv_fcn{}".format(layer_idx)
            module = Conv2d(next_feature, layer_features, 3, stride=1, padding=1)
            nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
            nn.init.constant_(module.bias, 0)
            self.add_module(layer_name, module)
            next_feature = layer_features
            self.blocks.append(layer_name)

开发者ID:mlperf，项目名称:training，代码行数:27，代码来源:roi_keypoint_feature_extractors.py

示例5: init

# 需要导入模块: from maskrcnn_benchmark.modeling import poolers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.poolers import Pooler [as 别名]
def __init__(self, cfg, in_channels):
        super(FPN2MLPFeatureExtractor, self).__init__()

        resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
        scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES
        sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
        # ##################### changed by hui #################################
        level_map = cfg.MODEL.ROI_BOX_HEAD.POOLER_LEVEL_MAP
        level_map_kwargs = cfg.MODEL.ROI_BOX_HEAD.POOLER_LEVEL_MAP_KWARGS
        pooler = Pooler(
            output_size=(resolution, resolution),
            scales=scales,
            sampling_ratio=sampling_ratio,
            level_map=level_map,
            level_map_kwargs=level_map_kwargs
        )
        # ########################################################################
        input_size = in_channels * resolution ** 2
        representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
        use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN
        self.pooler = pooler
        self.fc6 = make_fc(input_size, representation_size, use_gn)
        self.fc7 = make_fc(representation_size, representation_size, use_gn)
        self.out_channels = representation_size

开发者ID:ucas-vg，项目名称:TinyBenchmark，代码行数:26，代码来源:roi_box_feature_extractors.py

示例6: init

# 需要导入模块: from maskrcnn_benchmark.modeling import poolers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.poolers import Pooler [as 别名]
def __init__(self, cfg, in_channels):
        super(KeypointRCNNFeatureExtractor, self).__init__()

        resolution = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION
        scales = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SCALES
        sampling_ratio = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO
        pooler = Pooler(
            output_size=(resolution, resolution),
            scales=scales,
            sampling_ratio=sampling_ratio,
        )
        self.pooler = pooler

        input_features = in_channels
        layers = cfg.MODEL.ROI_KEYPOINT_HEAD.CONV_LAYERS
        next_feature = input_features
        self.blocks = []
        for layer_idx, layer_features in enumerate(layers, 1):
            layer_name = "conv_fcn{}".format(layer_idx)
            module = Conv2d(next_feature, layer_features, 3, stride=1, padding=1)
            nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
            nn.init.constant_(module.bias, 0)
            self.add_module(layer_name, module)
            next_feature = layer_features
            self.blocks.append(layer_name)
        self.out_channels = layer_features

开发者ID:Res2Net，项目名称:Res2Net-maskrcnn，代码行数:28，代码来源:roi_keypoint_feature_extractors.py

示例7: init

# 需要导入模块: from maskrcnn_benchmark.modeling import poolers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.poolers import Pooler [as 别名]
def __init__(self, cfg, in_channels):
        """
        Arguments:
            num_classes (int): number of output classes
            input_size (int): number of channels of the input once it's flattened
            representation_size (int): size of the intermediate representation
        """
        super(MaskRCNNFPNFeatureExtractor, self).__init__()

        resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION
        scales = cfg.MODEL.ROI_MASK_HEAD.POOLER_SCALES
        sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO
        pooler = Pooler(
            output_size=(resolution, resolution),
            scales=scales,
            sampling_ratio=sampling_ratio,
        )
        input_size = in_channels
        self.pooler = pooler

        use_gn = cfg.MODEL.ROI_MASK_HEAD.USE_GN
        layers = cfg.MODEL.ROI_MASK_HEAD.CONV_LAYERS
        dilation = cfg.MODEL.ROI_MASK_HEAD.DILATION

        next_feature = input_size
        self.blocks = []
        for layer_idx, layer_features in enumerate(layers, 1):
            layer_name = "mask_fcn{}".format(layer_idx)
            module = make_conv3x3(
                next_feature, layer_features,
                dilation=dilation, stride=1, use_gn=use_gn
            )
            self.add_module(layer_name, module)
            next_feature = layer_features
            self.blocks.append(layer_name)
        self.out_channels = layer_features

开发者ID:Res2Net，项目名称:Res2Net-maskrcnn，代码行数:38，代码来源:roi_mask_feature_extractors.py

注：本文中的maskrcnn_benchmark.modeling.poolers.Pooler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。

示例1: __init__

示例2: __init__

示例3: __init__

示例4: __init__

示例5: __init__

示例6: __init__