Python resnet_v1.resnet_v1_50方法代码示例

# 需要导入模块: from nets import resnet_v1 [as 别名]
# 或者: from nets.resnet_v1 import resnet_v1_50 [as 别名]
def __init__(self,
               is_training,
               first_stage_features_stride,
               reuse_weights=None,
               weight_decay=0.0):
    """Constructor.

    Args:
      is_training: See base class.
      first_stage_features_stride: See base class.
      reuse_weights: See base class.
      weight_decay: See base class.

    Raises:
      ValueError: If `first_stage_features_stride` is not 8 or 16,
        or if `architecture` is not supported.
    """
    super(FasterRCNNResnet50FeatureExtractor, self).__init__(
        'resnet_v1_50', resnet_v1.resnet_v1_50, is_training,
        first_stage_features_stride, reuse_weights, weight_decay) 

# 需要导入模块: from nets import resnet_v1 [as 别名]
# 或者: from nets.resnet_v1 import resnet_v1_50 [as 别名]
def __init__(self,
               is_training,
               first_stage_features_stride,
               batch_norm_trainable=False,
               reuse_weights=None,
               weight_decay=0.0):
    """Constructor.

    Args:
      is_training: See base class.
      first_stage_features_stride: See base class.
      batch_norm_trainable: See base class.
      reuse_weights: See base class.
      weight_decay: See base class.

    Raises:
      ValueError: If `first_stage_features_stride` is not 8 or 16,
        or if `architecture` is not supported.
    """
    super(FasterRCNNResnet50FeatureExtractor, self).__init__(
        'resnet_v1_50', resnet_v1.resnet_v1_50, is_training,
        first_stage_features_stride, batch_norm_trainable,
        reuse_weights, weight_decay) 

# 需要导入模块: from nets import resnet_v1 [as 别名]
# 或者: from nets.resnet_v1 import resnet_v1_50 [as 别名]
def __init__(self,
               is_training,
               first_stage_features_stride,
               batch_norm_trainable=False,
               reuse_weights=None,
               weight_decay=0.0,
               activation_fn=tf.nn.relu):
    """Constructor.

    Args:
      is_training: See base class.
      first_stage_features_stride: See base class.
      batch_norm_trainable: See base class.
      reuse_weights: See base class.
      weight_decay: See base class.
      activation_fn: See base class.

    Raises:
      ValueError: If `first_stage_features_stride` is not 8 or 16,
        or if `architecture` is not supported.
    """
    super(FasterRCNNResnet50FeatureExtractor,
          self).__init__('resnet_v1_50', resnet_v1.resnet_v1_50, is_training,
                         first_stage_features_stride, batch_norm_trainable,
                         reuse_weights, weight_decay, activation_fn) 

# 需要导入模块: from nets import resnet_v1 [as 别名]
# 或者: from nets.resnet_v1 import resnet_v1_50 [as 别名]
def __init__(self,
               is_training,
               first_stage_features_stride,
               reuse_weights=None,
               weight_decay=0.0,
               base_features='block3',
               freeze_layer='',
               batch_norm_trainable=False,
               ):
    """Constructor.

    Args:
      is_training: See base class.
      first_stage_features_stride: See base class.
      reuse_weights: See base class.
      weight_decay: See base class.

    Raises:
      ValueError: If `first_stage_features_stride` is not 8 or 16,
        or if `architecture` is not supported.
    """
    super(FasterRCNNResnet50FeatureExtractor, self).__init__(
        'resnet_v1_50', resnet_v1.resnet_v1_50, is_training,
        first_stage_features_stride, reuse_weights, weight_decay,
        base_features, freeze_layer, batch_norm_trainable) 

# 需要导入模块: from nets import resnet_v1 [as 别名]
# 或者: from nets.resnet_v1 import resnet_v1_50 [as 别名]
def __init__(self,
               is_training,
               depth_multiplier,
               min_depth,
               pad_to_multiple,
               conv_hyperparams,
               batch_norm_trainable=True,
               reuse_weights=None,
               use_explicit_padding=False,
               use_depthwise=False):
    """Resnet50 v1 FPN Feature Extractor for SSD Models.

    Args:
      is_training: whether the network is in training mode.
      depth_multiplier: float depth multiplier for feature extractor.
      min_depth: minimum feature extractor depth.
      pad_to_multiple: the nearest multiple to zero pad the input height and
        width dimensions to.
      conv_hyperparams: tf slim arg_scope for conv2d and separable_conv2d ops.
      batch_norm_trainable: Whether to update batch norm parameters during
        training or not. When training with a small batch size
        (e.g. 1), it is desirable to disable batch norm update and use
        pretrained batch norm params.
      reuse_weights: Whether to reuse variables. Default is None.
      use_explicit_padding: Whether to use explicit padding when extracting
        features. Default is False. UNUSED currently.
      use_depthwise: Whether to use depthwise convolutions. UNUSED currently.
    """
    super(SSDResnet50V1FpnFeatureExtractor, self).__init__(
        is_training, depth_multiplier, min_depth, pad_to_multiple,
        conv_hyperparams, resnet_v1.resnet_v1_50, 'resnet_v1_50', 'fpn',
        batch_norm_trainable, reuse_weights, use_explicit_padding) 

# 需要导入模块: from nets import resnet_v1 [as 别名]
# 或者: from nets.resnet_v1 import resnet_v1_50 [as 别名]
def __init__(self,
               is_training,
               depth_multiplier,
               min_depth,
               pad_to_multiple,
               conv_hyperparams_fn,
               reuse_weights=None,
               use_explicit_padding=False,
               use_depthwise=False,
               override_base_feature_extractor_hyperparams=False):
    """Resnet50 v1 Feature Extractor for SSD Models.

    Args:
      is_training: whether the network is in training mode.
      depth_multiplier: float depth multiplier for feature extractor.
      min_depth: minimum feature extractor depth.
      pad_to_multiple: the nearest multiple to zero pad the input height and
        width dimensions to.
      conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
        and separable_conv2d ops in the layers that are added on top of the
        base feature extractor.
      reuse_weights: Whether to reuse variables. Default is None.
      use_explicit_padding: Whether to use explicit padding when extracting
        features. Default is False.
      use_depthwise: Whether to use depthwise convolutions. Default is False.
      override_base_feature_extractor_hyperparams: Whether to override
        hyperparameters of the base feature extractor with the one from
        `conv_hyperparams_fn`.
    """
    super(SSDResnet50V1PpnFeatureExtractor, self).__init__(
        is_training, depth_multiplier, min_depth, pad_to_multiple,
        conv_hyperparams_fn, resnet_v1.resnet_v1_50, 'resnet_v1_50',
        reuse_weights, use_explicit_padding, use_depthwise,
        override_base_feature_extractor_hyperparams=(
            override_base_feature_extractor_hyperparams))