class_docstr: Default SSD validation transform.
  
  Parameters
  ----------
  width : int
      Image width.
  height : int
      Image height.
  mean : array-like of size 3
      Mean pixel values to be subtracted from image tensor. Default is [0.485, 0.456, 0.406].
  std : array-like of size 3
      Standard deviation to be divided from image. Default is [0.229, 0.224, 0.225].
class_docstr_tok: ["Default","SSD","validation","transform",".","Parameters","--","--","--","--","--","width",":","int","Image","width",".","height",":","int","Image","height",".","mean",":","array-like","of","size","3","Mean","pixel","values","to","be","subtracted","from","image","tensor",".","Default","is","[","0.485",",","0.456",",","0.406","]",".","std",":","array-like","of","size","3","Standard","deviation","to","be","divided","from","image",".","Default","is","[","0.229",",","0.224",",","0.225","]","."]
docstr: Apply transform to validation image/label.
docstr_tok: ["Apply","transform","to","validation","image\/label","."]
code: def __call__(self, src, label):
      """Apply transform to validation image/label."""
      # resize
      img, bbox = src.asnumpy(), label
      cv2 = try_import_cv2()
      input_h, input_w = self._height, self._width
      h, w, _ = src.shape
      s = max(h, w) * 1.0
      c = np.array([w / 2.0, h / 2.0], dtype=np.float32)
      trans_input = tbbox.get_affine_transform(
          c, s, 0, [input_w, input_h]
      )
      inp = cv2.warpAffine(
          img, trans_input, (input_w, input_h), flags=cv2.INTER_LINEAR
      )
      output_w = input_w
      output_h = input_h
      trans_output = tbbox.get_affine_transform(
          c, s, 0, [output_w, output_h]
      )
      for i in range(bbox.shape[0]):
          bbox[i, :2] = tbbox.affine_transform(
              bbox[i, :2], trans_output
          )
          bbox[i, 2:4] = tbbox.affine_transform(
              bbox[i, 2:4], trans_output
          )
      bbox[:, :2] = np.clip(bbox[:, :2], 0, output_w - 1)
      bbox[:, 2:4] = np.clip(bbox[:, 2:4], 0, output_h - 1)
      img = inp
  
      # to tensor
      img = img.astype(np.float32) / 255.0
      img = (img - self._mean) / self._std
      img = img.transpose(2, 0, 1).astype(np.float32)
      img = mx.nd.array(img)
      return img, bbox.astype(img.dtype)
  
file_path: gluon-cv/gluoncv/data/transforms/presets/center_net.py
imports: from __future__ import absolute_import
  import numpy
  import mxnet
  from ..None import bbox
  from ..None import image
  from ..None import experimental
  from ....utils.filesystem import try_import_cv2
label_desc: Use image node_id 2 for calling the CenterNetDefaultValTransform obj's underlying member method code with example usage: obj.__call__(src, label) and returns: img,  bbox