Python faster_rcnn_meta_arch.FasterRCNNFeatureExtractor方法代碼示例

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

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

  Returns:
    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor based on config.

  Raises:
    ValueError: On invalid feature extractor type.
  """
  feature_type = feature_extractor_config.type
  first_stage_features_stride = (
      feature_extractor_config.first_stage_features_stride)

  if feature_type not in FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP:
    raise ValueError('Unknown Faster R-CNN feature_extractor: {}'.format(
        feature_type))
  feature_extractor_class = FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP[
      feature_type]
  return feature_extractor_class(
      is_training, first_stage_features_stride, reuse_weights) 

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

    Note that this overrides the default implementation in
    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor which does not work for
    NASNet-A checkpoints.

    Args:
      first_stage_feature_extractor_scope: A scope name for the first stage
        feature extractor.
      second_stage_feature_extractor_scope: A scope name for the second stage
        feature extractor.

    Returns:
      A dict mapping variable names (to load from a checkpoint) to variables in
      the model graph.
    """
    # Note that the NAS checkpoint only contains the moving average version of
    # the Variables so we need to generate an appropriate dictionary mapping.
    variables_to_restore = {}
    for variable in tf.global_variables():
      if variable.op.name.startswith(
          first_stage_feature_extractor_scope):
        var_name = variable.op.name.replace(
            first_stage_feature_extractor_scope + '/', '')
        var_name += '/ExponentialMovingAverage'
        variables_to_restore[var_name] = variable
      if variable.op.name.startswith(
          second_stage_feature_extractor_scope):
        var_name = variable.op.name.replace(
            second_stage_feature_extractor_scope + '/', '')
        var_name += '/ExponentialMovingAverage'
        variables_to_restore[var_name] = variable
    return variables_to_restore 

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

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

    Args:
      first_stage_feature_extractor_scope: A scope name for the first stage
        feature extractor.
      second_stage_feature_extractor_scope: A scope name for the second 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(
          first_stage_feature_extractor_scope):
        var_name = variable.op.name.replace(
            first_stage_feature_extractor_scope + '/', '')
        var_name += '/ExponentialMovingAverage'
        variables_to_restore[var_name] = variable
      if variable.op.name.startswith(
          second_stage_feature_extractor_scope):
        var_name = variable.op.name.replace(
            second_stage_feature_extractor_scope + '/', '')
        var_name += '/ExponentialMovingAverage'
        variables_to_restore[var_name] = variable
    return variables_to_restore 

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

  Args:
    feature_extractor_config: A FasterRcnnFeatureExtractor proto config from
      faster_rcnn.proto.
    is_training: True if this feature extractor is being built for training.
    reuse_weights: if the feature extractor should reuse weights.
    inplace_batchnorm_update: Whether to update batch_norm inplace during
      training. This is required for batch norm to work correctly on TPUs. When
      this is false, user must add a control dependency on
      tf.GraphKeys.UPDATE_OPS for train/loss op in order to update the batch
      norm moving average parameters.

  Returns:
    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor based on config.

  Raises:
    ValueError: On invalid feature extractor type.
  """
  if inplace_batchnorm_update:
    raise ValueError('inplace batchnorm updates not supported.')
  feature_type = feature_extractor_config.type
  first_stage_features_stride = (
      feature_extractor_config.first_stage_features_stride)
  batch_norm_trainable = feature_extractor_config.batch_norm_trainable

  if feature_type not in FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP:
    raise ValueError('Unknown Faster R-CNN feature_extractor: {}'.format(
        feature_type))
  feature_extractor_class = FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP[
      feature_type]
  return feature_extractor_class(
      is_training, first_stage_features_stride,
      batch_norm_trainable, reuse_weights) 

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

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

  Returns:
    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor based on config.

  Raises:
    ValueError: On invalid feature extractor type.
  """
  feature_type = feature_extractor_config.type
  first_stage_features_stride = (
      feature_extractor_config.first_stage_features_stride)
  batch_norm_trainable = feature_extractor_config.batch_norm_trainable

  if feature_type not in FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP:
    raise ValueError('Unknown Faster R-CNN feature_extractor: {}'.format(
        feature_type))
  feature_extractor_class = FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP[
      feature_type]
  return feature_extractor_class(
      is_training, first_stage_features_stride,
      batch_norm_trainable, reuse_weights) 

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

    Note that this overrides the default implementation in
    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor which does not work for
    NASNet-A checkpoints.

    Args:
      first_stage_feature_extractor_scope: A scope name for the first stage
        feature extractor.
      second_stage_feature_extractor_scope: A scope name for the second stage
        feature extractor.

    Returns:
      A dict mapping variable names (to load from a checkpoint) to variables in
      the model graph.
    """
    # Note that the NAS checkpoint only contains the moving average version of
    # the Variables so we need to generate an appropriate dictionary mapping.
    variables_to_restore = {}
    for variable in variables_helper.get_global_variables_safely():
      if variable.op.name.startswith(
          first_stage_feature_extractor_scope):
        var_name = variable.op.name.replace(
            first_stage_feature_extractor_scope + '/', '')
        var_name += '/ExponentialMovingAverage'
        variables_to_restore[var_name] = variable
      if variable.op.name.startswith(
          second_stage_feature_extractor_scope):
        var_name = variable.op.name.replace(
            second_stage_feature_extractor_scope + '/', '')
        var_name += '/ExponentialMovingAverage'
        variables_to_restore[var_name] = variable
    return variables_to_restore