tfm.vision.maskrcnn_model.MaskRCNNModel

The Mask R-CNN(-RS) and Cascade RCNN-RS models.

tfm.vision.maskrcnn_model.MaskRCNNModel(
    backbone: tf.keras.Model,
    decoder: tf.keras.Model,
    rpn_head: tf.keras.layers.Layer,
    detection_head: Union[tf.keras.layers.Layer, List[tf.keras.layers.Layer]],
    roi_generator: tf.keras.layers.Layer,
    roi_sampler: Union[tf.keras.layers.Layer, List[tf.keras.layers.Layer]],
    roi_aligner: tf.keras.layers.Layer,
    detection_generator: tf.keras.layers.Layer,
    mask_head: Optional[tf.keras.layers.Layer] = None,
    mask_sampler: Optional[tf.keras.layers.Layer] = None,
    mask_roi_aligner: Optional[tf.keras.layers.Layer] = None,
    class_agnostic_bbox_pred: bool = False,
    cascade_class_ensemble: bool = False,
    min_level: Optional[int] = None,
    max_level: Optional[int] = None,
    num_scales: Optional[int] = None,
    aspect_ratios: Optional[List[float]] = None,
    anchor_size: Optional[float] = None,
    outer_boxes_scale: float = 1.0,
    **kwargs
)

backbone tf.keras.Model, the backbone network.
decoder tf.keras.Model, the decoder network.
rpn_head the RPN head.
detection_head the detection head or a list of heads.
roi_generator the ROI generator.
roi_sampler a single ROI sampler or a list of ROI samplers for cascade detection heads.
roi_aligner the ROI aligner.
detection_generator the detection generator.
mask_head the mask head.
mask_sampler the mask sampler.
mask_roi_aligner the ROI alginer for mask prediction.
class_agnostic_bbox_pred if True, perform class agnostic bounding box prediction. Needs to be True for Cascade RCNN models.
cascade_class_ensemble if True, ensemble classification scores over all detection heads.
min_level Minimum level in output feature maps.
max_level Maximum level in output feature maps.
num_scales A number representing intermediate scales added on each level. For instances, num_scales=2 adds one additional intermediate anchor scales [2^0, 2^0.5] on each level.
aspect_ratios A list representing the aspect raito anchors added on each level. The number indicates the ratio of width to height. For instances, aspect_ratios=[1.0, 2.0, 0.5] adds three anchors on each scale level.
anchor_size A number representing the scale of size of the base anchor to the feature stride 2^level.
outer_boxes_scale a float to scale up the bounding boxes to generate more inclusive masks. The scale is expected to be >=1.0.
**kwargs keyword arguments to be passed.

checkpoint_items Returns a dictionary of items to be additionally checkpointed.

Methods

call

View source

call(
    images: tf.Tensor,
    image_shape: tf.Tensor,
    anchor_boxes: Optional[Mapping[str, tf.Tensor]] = None,
    gt_boxes: Optional[tf.Tensor] = None,
    gt_classes: Optional[tf.Tensor] = None,
    gt_masks: Optional[tf.Tensor] = None,
    gt_outer_boxes: Optional[tf.Tensor] = None,
    training: Optional[bool] = None
) -> Mapping[str, Optional[tf.Tensor]]

Calls the model on new inputs and returns the outputs as tensors.

In this case call() just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Args
inputs Input tensor, or dict/list/tuple of input tensors.
training Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.
mask A mask or list of masks. A mask can be either a boolean tensor or None (no mask). For more details, check the guide here.

Returns
A tensor if there is a single output, or a list of tensors if there are more than one outputs.