code: """
  The following code is intended to be run only by travis for continuius intengration and testing
  purposes. For implementation examples see notebooks in the examples folder.
  """
  
  from PIL import Image, ImageDraw
  import torch
  from torch.utils.data import DataLoader
  from torchvision import transforms, datasets
  import numpy as np
  import pandas as pd
  from time import time
  import sys, os
  import glob
  
  from models.mtcnn import MTCNN, fixed_image_standardization
  from models.inception_resnet_v1 import InceptionResnetV1, get_torch_home
  
  
  #### CLEAR ALL OUTPUT FILES ####
  
  checkpoints = glob.glob(os.path.join(get_torch_home(), 'checkpoints/*'))
  for c in checkpoints:
      print('Removing {}'.format(c))
      os.remove(c)
  
  crop_files = glob.glob('data/test_images_aligned/**/*.png')
  for c in crop_files:
      print('Removing {}'.format(c))
      os.remove(c)
  
  
  #### TEST EXAMPLE IPYNB'S ####
  
  os.system('jupyter nbconvert --to script --stdout examples/infer.ipynb examples/finetune.ipynb > examples/tmptest.py')
  os.chdir('examples')
  try:
      import examples.tmptest
  except:
      import tmptest
  os.chdir('..')
  
  
  #### TEST MTCNN ####
  
  def get_image(path, trans):
      img = Image.open(path)
      img = trans(img)
      return img
  
  trans = transforms.Compose([
      transforms.Resize(512)
  ])
  
  trans_cropped = transforms.Compose([
      np.float32,
      transforms.ToTensor(),
      fixed_image_standardization
  ])
  
  dataset = datasets.ImageFolder('data/test_images', transform=trans)
  dataset.idx_to_class = {k: v for v, k in dataset.class_to_idx.items()}
  
  mtcnn_pt = MTCNN(device=torch.device('cpu'))
  
  names = []
  aligned = []
  aligned_fromfile = []
  for img, idx in dataset:
      name = dataset.idx_to_class[idx]
      start = time()
      img_align = mtcnn_pt(img, save_path='data/test_images_aligned/{}/1.png'.format(name))
      print('MTCNN time: {:6f} seconds'.format(time() - start))
      
      # Comparison between types
      img_box = mtcnn_pt.detect(img)[0]
      assert (img_box - mtcnn_pt.detect(np.array(img))[0]).sum() < 1e-2
      assert (img_box - mtcnn_pt.detect(torch.as_tensor(np.array(img)))[0]).sum() < 1e-2
  
      # Batching test
      assert (img_box - mtcnn_pt.detect([img, img])[0]).sum() < 1e-2
      assert (img_box - mtcnn_pt.detect(np.array([np.array(img), np.array(img)]))[0]).sum() < 1e-2
      assert (img_box - mtcnn_pt.detect(torch.as_tensor([np.array(img), np.array(img)]))[0]).sum() < 1e-2
  
      # Box selection
      mtcnn_pt.selection_method = 'probability'
      print('\nprobability - ', mtcnn_pt.detect(img))
      mtcnn_pt.selection_method = 'largest'
      print('largest - ', mtcnn_pt.detect(img))
      mtcnn_pt.selection_method = 'largest_over_theshold'
      print('largest_over_theshold - ', mtcnn_pt.detect(img))
      mtcnn_pt.selection_method = 'center_weighted_size'
      print('center_weighted_size - ', mtcnn_pt.detect(img))
  
      if img_align is not None:
          names.append(name)
          aligned.append(img_align)
          aligned_fromfile.append(get_image('data/test_images_aligned/{}/1.png'.format(name), trans_cropped))
  
  aligned = torch.stack(aligned)
  aligned_fromfile = torch.stack(aligned_fromfile)
  
  
  #### TEST EMBEDDINGS ####
  
  expected = [
      [
          [0.000000, 1.482895, 0.886342, 1.438450, 1.437583],
          [1.482895, 0.000000, 1.345686, 1.029880, 1.061939],
          [0.886342, 1.345686, 0.000000, 1.363125, 1.338803],
          [1.438450, 1.029880, 1.363125, 0.000000, 1.066040],
          [1.437583, 1.061939, 1.338803, 1.066040, 0.000000]
      ],
      [
          [0.000000, 1.430769, 0.992931, 1.414197, 1.329544],
          [1.430769, 0.000000, 1.253911, 1.144899, 1.079755],
          [0.992931, 1.253911, 0.000000, 1.358875, 1.337322],
          [1.414197, 1.144899, 1.358875, 0.000000, 1.204118],
          [1.329544, 1.079755, 1.337322, 1.204118, 0.000000]
      ]
  ]
  
  for i, ds in enumerate(['vggface2', 'casia-webface']):
      resnet_pt = InceptionResnetV1(pretrained=ds).eval()
  
      start = time()
      embs = resnet_pt(aligned)
      print('\nResnet time: {:6f} seconds\n'.format(time() - start))
  
      embs_fromfile = resnet_pt(aligned_fromfile)
  
      dists = [[(emb - e).norm().item() for e in embs] for emb in embs]
      dists_fromfile = [[(emb - e).norm().item() for e in embs_fromfile] for emb in embs_fromfile]
  
      print('\nOutput:')
      print(pd.DataFrame(dists, columns=names, index=names))
      print('\nOutput (from file):')
      print(pd.DataFrame(dists_fromfile, columns=names, index=names))
      print('\nExpected:')
      print(pd.DataFrame(expected[i], columns=names, index=names))
  
      total_error = (torch.tensor(dists) - torch.tensor(expected[i])).norm()
      total_error_fromfile = (torch.tensor(dists_fromfile) - torch.tensor(expected[i])).norm()
  
      print('\nTotal error: {}, {}'.format(total_error, total_error_fromfile))
  
      if sys.platform != 'win32':
          assert total_error < 1e-4
          assert total_error_fromfile < 1e-4
  
  
  #### TEST CLASSIFICATION ####
  
  resnet_pt = InceptionResnetV1(pretrained=ds, classify=True).eval()
  prob = resnet_pt(aligned)
  
  
  #### MULTI-FACE TEST ####
  
  mtcnn = MTCNN(keep_all=True)
  img = Image.open('data/multiface.jpg')
  boxes, probs = mtcnn.detect(img)
  
  draw = ImageDraw.Draw(img)
  for i, box in enumerate(boxes):
      draw.rectangle(box.tolist())
  
  mtcnn(img, save_path='data/tmp.png')
  
  
  #### MTCNN TYPES TEST ####
  
  img = Image.open('data/multiface.jpg')
  
  mtcnn = MTCNN(keep_all=True)
  boxes_ref, _ = mtcnn.detect(img)
  _ = mtcnn(img)
  
  mtcnn = MTCNN(keep_all=True).double()
  boxes_test, _ = mtcnn.detect(img)
  _ = mtcnn(img)
  
  box_diff = boxes_ref[np.argsort(boxes_ref[:,1])] - boxes_test[np.argsort(boxes_test[:,1])]
  total_error = np.sum(np.abs(box_diff))
  print('\nfp64 Total box error: {}'.format(total_error))
  
  assert total_error < 1e-2
  
  
  # half is not supported on CPUs, only GPUs
  if torch.cuda.is_available():
  
      mtcnn = MTCNN(keep_all=True, device='cuda').half()
      boxes_test, _ = mtcnn.detect(img)
      _ = mtcnn(img)
  
      box_diff = boxes_ref[np.argsort(boxes_ref[:,1])] - boxes_test[np.argsort(boxes_test[:,1])]
      print('fp16 Total box error: {}'.format(np.sum(np.abs(box_diff))))
  
      # test new automatic multi precision to compare
      if hasattr(torch.cuda, 'amp'):
          with torch.cuda.amp.autocast():
              mtcnn = MTCNN(keep_all=True, device='cuda')
              boxes_test, _ = mtcnn.detect(img)
              _ = mtcnn(img)
  
          box_diff = boxes_ref[np.argsort(boxes_ref[:,1])] - boxes_test[np.argsort(boxes_test[:,1])]
          print('AMP total box error: {}'.format(np.sum(np.abs(box_diff))))
  
              
  #### MULTI-IMAGE TEST ####
  
  mtcnn = MTCNN(keep_all=True)
  img = [
      Image.open('data/multiface.jpg'),
      Image.open('data/multiface.jpg')
  ]
  batch_boxes, batch_probs = mtcnn.detect(img)
  
  mtcnn(img, save_path=['data/tmp1.png', 'data/tmp1.png'])
  tmp_files = glob.glob('data/tmp*')
  for f in tmp_files:
      os.remove(f)
  
  
  #### NO-FACE TEST ####
  
  img = Image.new('RGB', (512, 512))
  mtcnn(img)
  mtcnn(img, return_prob=True)
  
apis: torch.stack pandas.DataFrame numpy.abs numpy.argsort torch.tensor torch.cuda.is_available torch.cuda.amp.autocast numpy.array torch.device