class_docstr: Multi-scale anchors class.
class_docstr_tok: ["Multi-scale","anchors","class","."]
docstr: Generate configurations of anchor boxes.
docstr_tok: ["Generate","configurations","of","anchor","boxes","."]
code: def _generate_configs(self):
      """Generate configurations of anchor boxes."""
      anchor_configs = {}
      feat_sizes = self.feat_sizes
      for level in range(self.min_level, self.max_level + 1):
          anchor_configs[level] = []
          for scale_octave in range(self.num_scales):
              for aspect in self.aspect_ratios:
                  anchor_configs[level].append(
                      (
                          (
                              feat_sizes[0]["height"]
                              / float(feat_sizes[level]["height"]),
                              feat_sizes[0]["width"]
                              / float(feat_sizes[level]["width"]),
                          ),
                          scale_octave / float(self.num_scales),
                          aspect,
                          self.anchor_scales[level - self.min_level],
                      )
                  )
      return anchor_configs
  
file_path: DALI/docs/examples/use_cases/tensorflow/efficientdet/pipeline/anchors.py
imports: import collections
  import numpy
  import tensorflow
  import utils
  from .anchors_utils import argmax_matcher
  from .anchors_utils import box_list
  from .anchors_utils import faster_rcnn_box_coder
  from .anchors_utils import region_similarity_calculator
  from .anchors_utils import target_assigner
label_desc: Use image node_id 2 for calling the Anchors obj's underlying member method code with example usage: obj._generate_configs() and returns: anchor_configs