code: import os
  import numpy as np
  from maskrcnn.lib.data.preprocessing import mold_inputs
  from maskrcnn.lib.config import cfg
  
  from maskrcnn.lib.utils import io_utils
  
  
  def test_mold_inputs_ones():
      image = np.ones((cfg.IMAGE.MAX_DIM, cfg.IMAGE.MAX_DIM, 3), dtype=np.uint8) * 255
      
      molded_images, image_metas = mold_inputs([image], cfg)
  
      mean = molded_images[0, 0, :, :].mean()
      assert abs(mean - ((1 - 0.485) / 0.229)) < 1e-5
  
      mean = molded_images[0, 1, :, :].mean()
      assert abs(mean - ((1 - 0.456) / 0.224)) < 1e-5
  
      mean = molded_images[0, 2, :, :].mean()
      assert abs(mean - ((1 - 0.406) / 0.225)) < 1e-5
  
      assert molded_images.shape == (1, 3, cfg.IMAGE.MAX_DIM, cfg.IMAGE.MAX_DIM)
  
      assert image_metas[0][1] == 1
  
  
  def test_mold_image():
      image_path = os.path.join("data", "COCO_val2014_000000018928.jpg")
      image = io_utils.read_image(image_path)
      molded_images, image_metas = mold_inputs([image], cfg)
  
      print("image_metas", image_metas)
      assert image_metas.shape[0] == 1
      assert molded_images.shape[1] == 3
      assert molded_images.shape == (1, 3, cfg.IMAGE.MAX_DIM, cfg.IMAGE.MAX_DIM)
  
      assert abs(image_metas[0][1] - 2.048) < 1e-10
  
apis: numpy.ones