Python ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor方法代码示例

本文整理汇总了Python中object_detection.models.ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor方法的典型用法代码示例。如果您正苦于以下问题：Python ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor方法的具体用法？Python ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor怎么用？Python ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor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object_detection.models.ssd_resnet_v1_fpn_feature_extractor的用法示例。

在下文中一共展示了ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor方法的5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_resnet_v1_fpn_feature_extractor [as 别名]
# 或者: from object_detection.models.ssd_resnet_v1_fpn_feature_extractor import SSDResnet152V1FpnFeatureExtractor [as 别名]
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
                                use_explicit_padding=False, min_depth=32,
                                use_keras=False):
    is_training = True
    if use_keras:
      return (ssd_resnet_v1_fpn_keras_feature_extractor.
              SSDResNet152V1FpnKerasFeatureExtractor(
                  is_training=is_training,
                  depth_multiplier=depth_multiplier,
                  min_depth=min_depth,
                  pad_to_multiple=pad_to_multiple,
                  conv_hyperparams=self._build_conv_hyperparams(
                      add_batch_norm=False),
                  freeze_batchnorm=False,
                  inplace_batchnorm_update=False,
                  name='ResNet152V1_FPN'))
    else:
      return (
          ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor(
              is_training, depth_multiplier, min_depth, pad_to_multiple,
              self.conv_hyperparams_fn,
              use_explicit_padding=use_explicit_padding))

开发者ID:ShivangShekhar，项目名称:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代码行数:24，代码来源:ssd_resnet_v1_fpn_feature_extractor_test.py

示例2: _create_feature_extractor

# 需要导入模块: from object_detection.models import ssd_resnet_v1_fpn_feature_extractor [as 别名]
# 或者: from object_detection.models.ssd_resnet_v1_fpn_feature_extractor import SSDResnet152V1FpnFeatureExtractor [as 别名]
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
                                use_explicit_padding=False):
    min_depth = 32
    is_training = True
    return (
        ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor(
            is_training,
            depth_multiplier,
            min_depth,
            pad_to_multiple,
            self.conv_hyperparams_fn,
            use_explicit_padding=use_explicit_padding))

开发者ID:ahmetozlu，项目名称:vehicle_counting_tensorflow，代码行数:14，代码来源:ssd_resnet_v1_fpn_feature_extractor_test.py

示例3: _create_feature_extractor

# 需要导入模块: from object_detection.models import ssd_resnet_v1_fpn_feature_extractor [as 别名]
# 或者: from object_detection.models.ssd_resnet_v1_fpn_feature_extractor import SSDResnet152V1FpnFeatureExtractor [as 别名]
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
                                use_explicit_padding=False):
    min_depth = 32
    conv_hyperparams = {}
    batch_norm_trainable = True
    is_training = True
    return (
        ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor(
            is_training, depth_multiplier, min_depth, pad_to_multiple,
            conv_hyperparams, batch_norm_trainable,
            use_explicit_padding=use_explicit_padding))

开发者ID:cagbal，项目名称:ros_people_object_detection_tensorflow，代码行数:13，代码来源:ssd_resnet_v1_fpn_feature_extractor_test.py

示例4: _create_feature_extractor

# 需要导入模块: from object_detection.models import ssd_resnet_v1_fpn_feature_extractor [as 别名]
# 或者: from object_detection.models.ssd_resnet_v1_fpn_feature_extractor import SSDResnet152V1FpnFeatureExtractor [as 别名]
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple):
    min_depth = 32
    conv_hyperparams = {}
    batch_norm_trainable = True
    is_training = True
    return (
        ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor(
            is_training, depth_multiplier, min_depth, pad_to_multiple,
            conv_hyperparams, batch_norm_trainable))

开发者ID:ShreyAmbesh，项目名称:Traffic-Rule-Violation-Detection-System，代码行数:11，代码来源:ssd_resnet_v1_fpn_feature_extractor_test.py

示例5: _create_feature_extractor

# 需要导入模块: from object_detection.models import ssd_resnet_v1_fpn_feature_extractor [as 别名]
# 或者: from object_detection.models.ssd_resnet_v1_fpn_feature_extractor import SSDResnet152V1FpnFeatureExtractor [as 别名]
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
                                use_explicit_padding=False, min_depth=32,
                                use_keras=False):
    is_training = True
    return (
        ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor(
            is_training, depth_multiplier, min_depth, pad_to_multiple,
            self.conv_hyperparams_fn,
            use_explicit_padding=use_explicit_padding))

开发者ID:tensorflow，项目名称:models，代码行数:11，代码来源:ssd_resnet_v1_fpn_feature_extractor_tf1_test.py

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