Python ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor方法代碼示例

本文整理匯總了Python中object_detection.models.ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor方法的典型用法代碼示例。如果您正苦於以下問題：Python ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor方法的具體用法？Python ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor怎麽用？Python ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.models.ssd_mobilenet_v1_feature_extractor的用法示例。

在下文中一共展示了ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: _create_feature_extractor

# 需要導入模塊: from object_detection.models import ssd_mobilenet_v1_feature_extractor [as 別名]
# 或者: from object_detection.models.ssd_mobilenet_v1_feature_extractor import SSDMobileNetV1FeatureExtractor [as 別名]
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
                                is_training=True, use_explicit_padding=False):
    """Constructs a new feature extractor.

    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.
      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.
    Returns:
      an ssd_meta_arch.SSDFeatureExtractor object.
    """
    min_depth = 32
    return ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor(
        is_training, depth_multiplier, min_depth, pad_to_multiple,
        self.conv_hyperparams_fn,
        use_explicit_padding=use_explicit_padding)

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:22，代碼來源:ssd_mobilenet_v1_feature_extractor_test.py

示例2: _create_feature_extractor

# 需要導入模塊: from object_detection.models import ssd_mobilenet_v1_feature_extractor [as 別名]
# 或者: from object_detection.models.ssd_mobilenet_v1_feature_extractor import SSDMobileNetV1FeatureExtractor [as 別名]
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
                                is_training=True, batch_norm_trainable=True,
                                use_explicit_padding=False):
    """Constructs a new feature extractor.

    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.
      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.
    Returns:
      an ssd_meta_arch.SSDFeatureExtractor object.
    """
    min_depth = 32
    with slim.arg_scope([slim.conv2d], normalizer_fn=slim.batch_norm) as sc:
      conv_hyperparams = sc
    return ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor(
        is_training, depth_multiplier, min_depth, pad_to_multiple,
        conv_hyperparams, batch_norm_trainable=batch_norm_trainable,
        use_explicit_padding=use_explicit_padding)

開發者ID:cagbal，項目名稱:ros_people_object_detection_tensorflow，代碼行數:27，代碼來源:ssd_mobilenet_v1_feature_extractor_test.py

示例3: _create_feature_extractor

# 需要導入模塊: from object_detection.models import ssd_mobilenet_v1_feature_extractor [as 別名]
# 或者: from object_detection.models.ssd_mobilenet_v1_feature_extractor import SSDMobileNetV1FeatureExtractor [as 別名]
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
                                is_training=True, batch_norm_trainable=True):
    """Constructs a new feature extractor.

    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_meta_arch.SSDFeatureExtractor object.
    """
    min_depth = 32
    with slim.arg_scope([slim.conv2d], normalizer_fn=slim.batch_norm) as sc:
      conv_hyperparams = sc
    return ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor(
        is_training, depth_multiplier, min_depth, pad_to_multiple,
        conv_hyperparams, batch_norm_trainable)

開發者ID:rky0930，項目名稱:yolo_v2，代碼行數:22，代碼來源:ssd_mobilenet_v1_feature_extractor_test.py

示例4: _create_feature_extractor

# 需要導入模塊: from object_detection.models import ssd_mobilenet_v1_feature_extractor [as 別名]
# 或者: from object_detection.models.ssd_mobilenet_v1_feature_extractor import SSDMobileNetV1FeatureExtractor [as 別名]
def _create_feature_extractor(self, depth_multiplier):
    """Constructs a new feature extractor.

    Args:
      depth_multiplier: float depth multiplier for feature extractor
    Returns:
      an ssd_meta_arch.SSDFeatureExtractor object.
    """
    min_depth = 32
    conv_hyperparams = {}
    return ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor(
        depth_multiplier, min_depth, conv_hyperparams)

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:14，代碼來源:ssd_mobilenet_v1_feature_extractor_test.py

示例5: _create_feature_extractor

# 需要導入模塊: from object_detection.models import ssd_mobilenet_v1_feature_extractor [as 別名]
# 或者: from object_detection.models.ssd_mobilenet_v1_feature_extractor import SSDMobileNetV1FeatureExtractor [as 別名]
def _create_feature_extractor(self,
                                depth_multiplier,
                                pad_to_multiple,
                                use_explicit_padding=False,
                                num_layers=6,
                                is_training=False,
                                use_keras=False):
    """Constructs a new feature extractor.

    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.
      use_keras: if True builds a keras-based feature extractor, if False builds
        a slim-based one.

    Returns:
      an ssd_meta_arch.SSDFeatureExtractor object.
    """
    min_depth = 32
    del use_keras
    return ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor(
        is_training,
        depth_multiplier,
        min_depth,
        pad_to_multiple,
        self.conv_hyperparams_fn,
        use_explicit_padding=use_explicit_padding,
        num_layers=num_layers)

開發者ID:tensorflow，項目名稱:models，代碼行數:36，代碼來源:ssd_mobilenet_v1_feature_extractor_tf1_test.py

示例6: _create_feature_extractor

# 需要導入模塊: from object_detection.models import ssd_mobilenet_v1_feature_extractor [as 別名]
# 或者: from object_detection.models.ssd_mobilenet_v1_feature_extractor import SSDMobileNetV1FeatureExtractor [as 別名]
def _create_feature_extractor(self,
                                depth_multiplier,
                                pad_to_multiple,
                                use_explicit_padding=False,
                                num_layers=6,
                                is_training=False,
                                use_keras=False):
    """Constructs a new feature extractor.

    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.
      use_keras: if True builds a keras-based feature extractor, if False builds
        a slim-based one.

    Returns:
      an ssd_meta_arch.SSDFeatureExtractor object.
    """
    min_depth = 32
    if use_keras:
      return (ssd_mobilenet_v1_keras_feature_extractor
              .SSDMobileNetV1KerasFeatureExtractor(
                  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,
                  use_explicit_padding=use_explicit_padding,
                  num_layers=num_layers,
                  name='MobilenetV1'))
    else:
      return ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor(
          is_training,
          depth_multiplier,
          min_depth,
          pad_to_multiple,
          self.conv_hyperparams_fn,
          use_explicit_padding=use_explicit_padding,
          num_layers=num_layers)

開發者ID:ShivangShekhar，項目名稱:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代碼行數:50，代碼來源:ssd_mobilenet_v1_feature_extractor_test.py

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