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Python ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor方法代码示例

本文整理汇总了Python中object_detection.models.ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor方法的典型用法代码示例。如果您正苦于以下问题:Python ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor方法的具体用法?Python ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor怎么用?Python ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor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object_detection.models.ssd_inception_v2_feature_extractor的用法示例。


在下文中一共展示了ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor方法的4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: _create_feature_extractor

# 需要导入模块: from object_detection.models import ssd_inception_v2_feature_extractor [as 别名]
# 或者: from object_detection.models.ssd_inception_v2_feature_extractor import SSDInceptionV2FeatureExtractor [as 别名]
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
                                is_training=True):
    """Constructs a SsdInceptionV2FeatureExtractor.

    Args:
      depth_multiplier: float depth multiplier for feature extractor
      pad_to_multiple: the nearest multiple to zero pad the input height and
        width dimensions to.
      is_training: whether the network is in training mode.

    Returns:
      an ssd_inception_v2_feature_extractor.SsdInceptionV2FeatureExtractor.
    """
    min_depth = 32
    return ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor(
        is_training, depth_multiplier, min_depth, pad_to_multiple,
        self.conv_hyperparams_fn,
        override_base_feature_extractor_hyperparams=True) 
开发者ID:ahmetozlu,项目名称:vehicle_counting_tensorflow,代码行数:20,代码来源:ssd_inception_v2_feature_extractor_test.py

示例2: _create_feature_extractor

# 需要导入模块: from object_detection.models import ssd_inception_v2_feature_extractor [as 别名]
# 或者: from object_detection.models.ssd_inception_v2_feature_extractor import SSDInceptionV2FeatureExtractor [as 别名]
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
                                is_training=True, batch_norm_trainable=True):
    """Constructs a SsdInceptionV2FeatureExtractor.

    Args:
      depth_multiplier: float depth multiplier for feature extractor
      pad_to_multiple: the nearest multiple to zero pad the input height and
        width dimensions to.
      is_training: whether the network is in training mode.
      batch_norm_trainable: Whether to update batch norm parameters during
        training or not
    Returns:
      an ssd_inception_v2_feature_extractor.SsdInceptionV2FeatureExtractor.
    """
    min_depth = 32
    conv_hyperparams = {}
    return ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor(
        is_training, depth_multiplier, min_depth, pad_to_multiple,
        conv_hyperparams, batch_norm_trainable) 
开发者ID:cagbal,项目名称:ros_people_object_detection_tensorflow,代码行数:21,代码来源:ssd_inception_v2_feature_extractor_test.py

示例3: _create_feature_extractor

# 需要导入模块: from object_detection.models import ssd_inception_v2_feature_extractor [as 别名]
# 或者: from object_detection.models.ssd_inception_v2_feature_extractor import SSDInceptionV2FeatureExtractor [as 别名]
def _create_feature_extractor(self, depth_multiplier):
    """Constructs a SsdInceptionV2FeatureExtractor.

    Args:
      depth_multiplier: float depth multiplier for feature extractor
    Returns:
      an ssd_inception_v2_feature_extractor.SsdInceptionV2FeatureExtractor.
    """
    min_depth = 32
    conv_hyperparams = {}
    return ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor(
        depth_multiplier, min_depth, conv_hyperparams) 
开发者ID:ringringyi,项目名称:DOTA_models,代码行数:14,代码来源:ssd_inception_v2_feature_extractor_test.py

示例4: _create_feature_extractor

# 需要导入模块: from object_detection.models import ssd_inception_v2_feature_extractor [as 别名]
# 或者: from object_detection.models.ssd_inception_v2_feature_extractor import SSDInceptionV2FeatureExtractor [as 别名]
def _create_feature_extractor(self,
                                depth_multiplier,
                                pad_to_multiple,
                                use_explicit_padding=False,
                                num_layers=6,
                                is_training=True):
    """Constructs a SsdInceptionV2FeatureExtractor.

    Args:
      depth_multiplier: float depth multiplier for feature extractor
      pad_to_multiple: the nearest multiple to zero pad the input height and
        width dimensions to.
      use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
        inputs so that the output dimensions are the same as if 'SAME' padding
        were used.
      num_layers: number of SSD layers.
      is_training: whether the network is in training mode.

    Returns:
      an ssd_inception_v2_feature_extractor.SsdInceptionV2FeatureExtractor.
    """
    min_depth = 32
    return ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor(
        is_training,
        depth_multiplier,
        min_depth,
        pad_to_multiple,
        self.conv_hyperparams_fn,
        num_layers=num_layers,
        override_base_feature_extractor_hyperparams=True) 
开发者ID:ShivangShekhar,项目名称:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV,代码行数:32,代码来源:ssd_inception_v2_feature_extractor_test.py


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