Python ssd_meta_arch.SSDFeatureExtractor方法代碼示例

本文整理匯總了Python中object_detection.meta_architectures.ssd_meta_arch.SSDFeatureExtractor方法的典型用法代碼示例。如果您正苦於以下問題：Python ssd_meta_arch.SSDFeatureExtractor方法的具體用法？Python ssd_meta_arch.SSDFeatureExtractor怎麽用？Python ssd_meta_arch.SSDFeatureExtractor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.meta_architectures.ssd_meta_arch的用法示例。

在下文中一共展示了ssd_meta_arch.SSDFeatureExtractor方法的7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: restore_from_classification_checkpoint_fn

# 需要導入模塊: from object_detection.meta_architectures import ssd_meta_arch [as 別名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 別名]
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
    """Returns a map of variables to load from a foreign checkpoint.

    Note that this overrides the default implementation in
    ssd_meta_arch.SSDFeatureExtractor which does not work for PNASNet
    checkpoints.

    Args:
      feature_extractor_scope: A scope name for the first stage feature
        extractor.

    Returns:
      A dict mapping variable names (to load from a checkpoint) to variables in
      the model graph.
    """
    variables_to_restore = {}
    for variable in tf.global_variables():
      if variable.op.name.startswith(feature_extractor_scope):
        var_name = variable.op.name.replace(feature_extractor_scope + '/', '')
        var_name += '/ExponentialMovingAverage'
        variables_to_restore[var_name] = variable
    return variables_to_restore

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:24，代碼來源:ssd_pnasnet_feature_extractor.py

示例2: restore_from_classification_checkpoint_fn

# 需要導入模塊: from object_detection.meta_architectures import ssd_meta_arch [as 別名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 別名]
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
    """Returns a map of variables to load from a foreign checkpoint.

    Note that this overrides the default implementation in
    ssd_meta_arch.SSDFeatureExtractor which does not work for PNASNet
    checkpoints.

    Args:
      feature_extractor_scope: A scope name for the first stage feature
        extractor.

    Returns:
      A dict mapping variable names (to load from a checkpoint) to variables in
      the model graph.
    """
    variables_to_restore = {}
    for variable in variables_helper.get_global_variables_safely():
      if variable.op.name.startswith(feature_extractor_scope):
        var_name = variable.op.name.replace(feature_extractor_scope + '/', '')
        var_name += '/ExponentialMovingAverage'
        variables_to_restore[var_name] = variable
    return variables_to_restore

開發者ID:ShivangShekhar，項目名稱:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代碼行數:24，代碼來源:ssd_pnasnet_feature_extractor.py

示例3: _build_ssd_feature_extractor

# 需要導入模塊: from object_detection.meta_architectures import ssd_meta_arch [as 別名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 別名]
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
                                 reuse_weights=None):
  """Builds a ssd_meta_arch.SSDFeatureExtractor based on config.

  Args:
    feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
    is_training: True if this feature extractor is being built for training.
    reuse_weights: if the feature extractor should reuse weights.

  Returns:
    ssd_meta_arch.SSDFeatureExtractor based on config.

  Raises:
    ValueError: On invalid feature extractor type.
  """
  feature_type = feature_extractor_config.type
  depth_multiplier = feature_extractor_config.depth_multiplier
  min_depth = feature_extractor_config.min_depth
  conv_hyperparams = hyperparams_builder.build(
      feature_extractor_config.conv_hyperparams, is_training)

  if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
    raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))

  feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
  return feature_extractor_class(depth_multiplier, min_depth, conv_hyperparams,
                                 reuse_weights)

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:29，代碼來源:model_builder.py

示例4: _build_ssd_feature_extractor

# 需要導入模塊: from object_detection.meta_architectures import ssd_meta_arch [as 別名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 別名]
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
                                 reuse_weights=None):
  """Builds a ssd_meta_arch.SSDFeatureExtractor based on config.

  Args:
    feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
    is_training: True if this feature extractor is being built for training.
    reuse_weights: if the feature extractor should reuse weights.

  Returns:
    ssd_meta_arch.SSDFeatureExtractor based on config.

  Raises:
    ValueError: On invalid feature extractor type.
  """
  feature_type = feature_extractor_config.type
  depth_multiplier = feature_extractor_config.depth_multiplier
  min_depth = feature_extractor_config.min_depth
  pad_to_multiple = feature_extractor_config.pad_to_multiple
  batch_norm_trainable = feature_extractor_config.batch_norm_trainable
  use_explicit_padding = feature_extractor_config.use_explicit_padding
  use_depthwise = feature_extractor_config.use_depthwise
  conv_hyperparams = hyperparams_builder.build(
      feature_extractor_config.conv_hyperparams, is_training)

  if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
    raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))

  feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
  return feature_extractor_class(is_training, depth_multiplier, min_depth,
                                 pad_to_multiple, conv_hyperparams,
                                 batch_norm_trainable, reuse_weights,
                                 use_explicit_padding, use_depthwise)

開發者ID:cagbal，項目名稱:ros_people_object_detection_tensorflow，代碼行數:35，代碼來源:model_builder.py

示例5: _build_ssd_feature_extractor

# 需要導入模塊: from object_detection.meta_architectures import ssd_meta_arch [as 別名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 別名]
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
                                 reuse_weights=None):
  """Builds a ssd_meta_arch.SSDFeatureExtractor based on config.

  Args:
    feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
    is_training: True if this feature extractor is being built for training.
    reuse_weights: if the feature extractor should reuse weights.

  Returns:
    ssd_meta_arch.SSDFeatureExtractor based on config.

  Raises:
    ValueError: On invalid feature extractor type.
  """
  feature_type = feature_extractor_config.type
  depth_multiplier = feature_extractor_config.depth_multiplier
  min_depth = feature_extractor_config.min_depth
  pad_to_multiple = feature_extractor_config.pad_to_multiple
  use_explicit_padding = feature_extractor_config.use_explicit_padding
  use_depthwise = feature_extractor_config.use_depthwise
  conv_hyperparams = hyperparams_builder.build(
      feature_extractor_config.conv_hyperparams, is_training)
  override_base_feature_extractor_hyperparams = (
      feature_extractor_config.override_base_feature_extractor_hyperparams)

  if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
    raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))

  feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
  return feature_extractor_class(
      is_training, depth_multiplier, min_depth, pad_to_multiple,
      conv_hyperparams, reuse_weights, use_explicit_padding, use_depthwise,
      override_base_feature_extractor_hyperparams)

開發者ID:ambakick，項目名稱:Person-Detection-and-Tracking，代碼行數:36，代碼來源:model_builder.py

示例6: _build_ssd_feature_extractor

# 需要導入模塊: from object_detection.meta_architectures import ssd_meta_arch [as 別名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 別名]
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
                                 reuse_weights=None):
  """Builds a ssd_meta_arch.SSDFeatureExtractor based on config.

  Args:
    feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
    is_training: True if this feature extractor is being built for training.
    reuse_weights: if the feature extractor should reuse weights.

  Returns:
    ssd_meta_arch.SSDFeatureExtractor based on config.

  Raises:
    ValueError: On invalid feature extractor type.
  """
  feature_type = feature_extractor_config.type
  depth_multiplier = feature_extractor_config.depth_multiplier
  min_depth = feature_extractor_config.min_depth
  pad_to_multiple = feature_extractor_config.pad_to_multiple
  batch_norm_trainable = feature_extractor_config.batch_norm_trainable
  conv_hyperparams = hyperparams_builder.build(
      feature_extractor_config.conv_hyperparams, is_training)

  if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
    raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))

  feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
  return feature_extractor_class(is_training, depth_multiplier, min_depth,
                                 pad_to_multiple, conv_hyperparams,
                                 batch_norm_trainable, reuse_weights)

開發者ID:rky0930，項目名稱:yolo_v2，代碼行數:32，代碼來源:model_builder.py

示例7: _build_ssd_feature_extractor

# 需要導入模塊: from object_detection.meta_architectures import ssd_meta_arch [as 別名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 別名]
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
                                 reuse_weights=None):
  """Builds a ssd_meta_arch.SSDFeatureExtractor based on config.

  Args:
    feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
    is_training: True if this feature extractor is being built for training.
    reuse_weights: if the feature extractor should reuse weights.

  Returns:
    ssd_meta_arch.SSDFeatureExtractor based on config.

  Raises:
    ValueError: On invalid feature extractor type.
  """
  feature_type = feature_extractor_config.type
  depth_multiplier = feature_extractor_config.depth_multiplier
  min_depth = feature_extractor_config.min_depth
  pad_to_multiple = feature_extractor_config.pad_to_multiple
  use_explicit_padding = feature_extractor_config.use_explicit_padding
  use_depthwise = feature_extractor_config.use_depthwise
  conv_hyperparams = hyperparams_builder.build(
      feature_extractor_config.conv_hyperparams, is_training)
  override_base_feature_extractor_hyperparams = (
      feature_extractor_config.override_base_feature_extractor_hyperparams)

  if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
    raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))

  feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
  kwargs = {
      'is_training':
          is_training,
      'depth_multiplier':
          depth_multiplier,
      'min_depth':
          min_depth,
      'pad_to_multiple':
          pad_to_multiple,
      'conv_hyperparams_fn':
          conv_hyperparams,
      'reuse_weights':
          reuse_weights,
      'use_explicit_padding':
          use_explicit_padding,
      'use_depthwise':
          use_depthwise,
      'override_base_feature_extractor_hyperparams':
          override_base_feature_extractor_hyperparams
  }

  if feature_extractor_config.HasField('fpn'):
    kwargs.update({
        'fpn_min_level': feature_extractor_config.fpn.min_level,
        'fpn_max_level': feature_extractor_config.fpn.max_level,
    })

  return feature_extractor_class(**kwargs)

開發者ID:BMW-InnovationLab，項目名稱:BMW-TensorFlow-Training-GUI，代碼行數:60，代碼來源:model_builder.py

注：本文中的object_detection.meta_architectures.ssd_meta_arch.SSDFeatureExtractor方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。