tfm.vision.layers.MultilevelDetectionGenerator

Generates detected boxes with scores and classes for one-stage detector.

tfm.vision.layers.MultilevelDetectionGenerator(
    apply_nms: bool = True,
    pre_nms_top_k: int = 5000,
    pre_nms_score_threshold: float = 0.05,
    nms_iou_threshold: float = 0.5,
    max_num_detections: int = 100,
    nms_version: str = 'v1',
    use_cpu_nms: bool = False,
    soft_nms_sigma: Optional[float] = None,
    tflite_post_processing_config: Optional[Dict[str, Any]] = None,
    **kwargs
)

apply_nms A bool of whether or not apply non maximum suppression. If False, the decoded boxes and their scores are returned.
pre_nms_top_k An int of the number of top scores proposals to be kept before applying NMS.
pre_nms_score_threshold A float of the score threshold to apply before applying NMS. Proposals whose scores are below this threshold are thrown away.
nms_iou_threshold A float in [0, 1], the NMS IoU threshold.
max_num_detections An int of the final number of total detections to generate.
nms_version A string of batched, v1 or v2 specifies NMS version
use_cpu_nms A bool of whether or not enforce NMS to run on CPU.
soft_nms_sigma A float representing the sigma parameter for Soft NMS. When soft_nms_sigma=0.0, we fall back to standard NMS.
tflite_post_processing_config An optional dictionary containing post-processing parameters used for TFLite custom NMS op.
**kwargs Additional keyword arguments passed to Layer.

Methods

call

call(
    inputs, *args, **kwargs
)

This is where the layer's logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances, in __init__(), or in the build() method that is called automatically before call() executes for the first time.

Args
inputs Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules:

  • inputs must be explicitly passed. A layer cannot have zero arguments, and inputs cannot be provided via the default value of a keyword argument.
  • NumPy array or Python scalar values in inputs get cast as tensors.
  • Keras mask metadata is only collected from inputs.
  • Layers are built (build(input_shape) method) using shape info from inputs only.
  • input_spec compatibility is only checked against inputs.
  • Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.
  • The SavedModel input specification is generated using inputs only.
  • Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.
*args Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.
**kwargs Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved:
  • training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference.
  • mask: Boolean input mask. If the layer's call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

    • Returns
    A tensor or list/tuple of tensors.