class_docstr: BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs,
             see https://arxiv.org/abs/1907.05047
  
  Args:
      backbone (nn.Layer): backbone instance
      neck (nn.Layer): neck instance
      blaze_head (nn.Layer): `blazeHead` instance
      post_process (object): `BBoxPostProcess` instance
class_docstr_tok: ["BlazeFace",":","Sub-millisecond","Neural","Face","Detection","on","Mobile","GPUs",",","see","https",":","\/\/arxiv.org\/abs\/1907.05047","Args",":","backbone","(","nn.Layer",")",":","backbone","instance","neck","(","nn.Layer",")",":","neck","instance","blaze_head","(","nn.Layer",")",":","`","blazeHead","`","instance","post_process","(","object",")",":","`","BBoxPostProcess","`","instance"]
code: def from_config(cls, cfg, *args, **kwargs):
      # backbone
      backbone = create(cfg["backbone"])
      # fpn
      kwargs = {"input_shape": backbone.out_shape}
      neck = create(cfg["neck"], **kwargs)
      # head
      kwargs = {"input_shape": neck.out_shape}
      blaze_head = create(cfg["blaze_head"], **kwargs)
  
      return {
          "backbone": backbone,
          "neck": neck,
          "blaze_head": blaze_head,
      }
  
file_path: PaddleDetection/ppdet/modeling/architectures/blazeface.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from ppdet.core.workspace import register, create
  from .meta_arch import BaseArch
  import paddle
  import paddle.nn.functional
label_desc: Use image node_id 2 for calling the BlazeFace obj's underlying member method code with example usage: obj.from_config(cls, cfg) and returns: dict