Documentation

Restrictions

  • If the dynamic AIPP and dynamic batch size are both enabled:
    • The batchSize parameter passed to the acl.mdl.create_aipp call must be set to the allowed maximum profile.
    • For data nodes that require dynamic AIPP, the corresponding input buffer must be allocated based on the maximum batch size profile.
  • If the dynamic AIPP and dynamic image size are both enabled:
    • If the image cropping, resizing, or padding function is enabled in setting the dynamic AIPP parameters, the dynamic image size becomes unavailable.
    • If the image cropping, resizing, or padding function is not enabled in setting the dynamic AIPP parameters, the dynamic AIPP and dynamic image size are both available. However, ensure that the width and height configured by calling acl.mdl.set_aipp_src_image_size are consistent with those configured by calling acl.mdl.set_dynamic_hw_size.
    • For data nodes that require dynamic AIPP, the corresponding input buffer must be allocated based on the allowed maximum image size.
  • When dynamic AIPP and dynamic shape input (setting the shape range) are used at the same time, the width and height of the output image of dynamic AIPP must be within the configured shape range.
  • For a single model, AIPP (static or dynamic) and dynamic dimensions (ND format) are mutually exclusive.
  • pyACL also supports Digital Vision Pre-Processing (DVPP), which introduces hardware-based media data processing techniques, including resizing, cropping, format conversion, image encoding and decoding, and video encoding and decoding. Compared with AIPP, DVPP offers a wider set of processing operations, but it has particular restrictions on the image input/output and memory allocation.

    For details about the DVPP APIs, see DVPP Image/Video Processing (Media Data Processing).