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
)

Args
`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

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].

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

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].

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].