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tfm.vision.heads.RetinaNetHead

Creates a RetinaNet head.

tfm.vision.heads.RetinaNetHead(
    min_level: int,
    max_level: int,
    num_classes: int,
    num_anchors_per_location: int,
    num_convs: int = 4,
    num_filters: int = 256,
    attribute_heads: Optional[List[Dict[str, Any]]] = None,
    share_classification_heads: bool = False,
    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,
    num_params_per_anchor: int = 4,
    share_level_convs: bool = True,
    **kwargs
)

Args
`min_level`	An `int` number of minimum feature level.
`max_level`	An `int` number of maximum feature level.
`num_classes`	An `int` number of classes to predict.
`num_anchors_per_location`	An `int` number of anchors per pixel location.
`num_convs`	An `int` number that represents the number of the intermediate conv layers before the prediction.
`num_filters`	An `int` number that represents the number of filters of the intermediate conv layers.
`attribute_heads`	If not None, a list that contains a dict for each additional attribute head. Each dict consists of 4 key-value pairs: `name`, `type` ('regression' or 'classification'), `size` (number of predicted values for each instance), and `prediction_tower_name` (optional, specifies shared prediction towers.)
`share_classification_heads`	A `bool` that indicates whether sharing weights among the main and attribute classification heads.
`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.
`num_params_per_anchor`	Number of parameters required to specify an anchor box. For example, `num_params_per_anchor` would be 4 for axis-aligned anchor boxes specified by their y-centers, x-centers, heights, and widths.
`share_level_convs`	An optional bool to enable sharing convs across levels for classnet, boxnet, classifier and box regressor. If True, convs will be shared across all levels.
`**kwargs`	Additional keyword arguments to be passed.

Methods

`call`

View source

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

Forward pass of the RetinaNet 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, num_params_per_anchor * num_anchors_per_location].
`attributes`	a dict of (attribute_name, attribute_prediction). Each `attribute_prediction` is a dict of: key: `str`, the level of the multilevel predictions. values: `Tensor`, the box scores predicted from a particular feature level, whose shape is [batch, height_l, width_l, attribute_size * num_anchors_per_location]. Can be an empty dictionary if no attribute learning is required.

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, num_params_per_anchor * num_anchors_per_location].

attributes a dict of (attribute_name, attribute_prediction). Each attribute_prediction is a dict of:
key: str, the level of the multilevel predictions.

values: Tensor, the box scores predicted from a particular feature level, whose shape is [batch, height_l, width_l, attribute_size * num_anchors_per_location]. Can be an empty dictionary if no attribute learning is required.