Dynamic Batching feature allows you+ to dynamically change batch size for inference calls within preset batch size limit. This feature might be useful when batch size is unknown beforehand, and using extra large batch size is undesired or impossible due to resource limitations. For example, face detection with person age, gender, or mood recognition is a typical usage scenario.
You can activate Dynamic Batching by setting
KEY_DYN_BATCH_ENABLED flag to
YES in a configuration map that is passed to the plugin while loading a network. This configuration creates an
ExecutableNetwork object that will allow setting batch size dynamically in all of its infer requests using
SetBatch() method. The batch size that was set in passed
CNNNetwork object will be used as a maximum batch size limit.
Here is a code example:
Currently, certain limitations for using Dynamic Batching exist:
Do not use layers that might arbitrary change tensor shape (such as Flatten, Permute, Reshape), layers specific to object detection topologies (ROIPooling, ProirBox, DetectionOutput), and custom layers. Topology analysis is performed during the process of loading a network into plugin, and if topology is not applicable, an exception is generated.