tfm.vision.heads.RPNHead

Creates a Region Proposal Network (RPN) head.

tfm.vision.heads.RPNHead(
    min_level: int,
    max_level: int,
    num_anchors_per_location: int,
    num_convs: int = 1,
    num_filters: int = 256,
    use_separable_conv: bool = False,
    activation: str = 'relu',
    use_sync_bn: bool = False,
    norm_momentum: float = 0.99,
    norm_epsilon: float = 0.001,
    kernel_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
    bias_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
    **kwargs
)

min_level An int number of minimum feature level.
max_level An int number of maximum feature level.
num_anchors_per_location An int number of number of anchors per pixel location.
num_convs An int number that represents the number of the intermediate convolution layers before the prediction.
num_filters An int number that represents the number of filters of the intermediate convolution layers.
use_separable_conv A bool that indicates whether the separable convolution layers is used.
activation A str that indicates which activation is used, e.g. 'relu', 'swish', etc.
use_sync_bn A bool that indicates whether to use synchronized batch normalization across different replicas.
norm_momentum A float of normalization momentum for the moving average.
norm_epsilon A float added to variance to avoid dividing by zero.
kernel_regularizer A tf_keras.regularizers.Regularizer object for Conv2D. Default is None.
bias_regularizer A tf_keras.regularizers.Regularizer object for Conv2D.
**kwargs Additional keyword arguments to be passed.

Methods

call

View source

call(
    features: Mapping[str, tf.Tensor]
)

Forward pass of the RPN head.

Args
features A dict of tf.Tensor where

  • key: A str of the level of the multilevel features.
  • values: A tf.Tensor, the feature map tensors, whose shape is [batch, height_l, width_l, channels].

Returns
scores A dict of tf.Tensor which includes scores of the predictions.

  • key: A str of the level of the multilevel predictions.
  • values: A tf.Tensor of the box scores predicted from a particular feature level, whose shape is [batch, height_l, width_l, num_classes * num_anchors_per_location].
boxes A dict of tf.Tensor which includes coordinates of the predictions.
  • key: A str of the level of the multilevel predictions.
  • values: A tf.Tensor of the box scores predicted from a particular feature level, whose shape is [batch, height_l, width_l, 4 * num_anchors_per_location].