text: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  
  import sys
  import os
  import numpy as np
  import mxnet as mx
  from mxnet.test_utils import *
  curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
  sys.path.insert(0, os.path.join(curr_path, '../unittest'))
  from common import setup_module, with_seed, teardown_module
  from mxnet.gluon import utils
  import tarfile
  
  def _get_model():
      if not os.path.exists('model/Inception-7-symbol.json'):
          download('http://data.mxnet.io/models/imagenet/inception-v3.tar.gz')
          with tarfile.open(name="inception-v3.tar.gz", mode="r:gz") as tf:
              tf.extractall()
  
  def _dump_images(shape):
      import skimage.io
      import skimage.transform
      img_list = []
      for img in sorted(os.listdir('data/test_images/')):
          img = skimage.io.imread('data/test_images/'+img)
          short_egde = min(img.shape[:2])
          yy = int((img.shape[0] - short_egde) / 2)
          xx = int((img.shape[1] - short_egde) / 2)
          img = img[yy : yy + short_egde, xx : xx + short_egde]
          img = skimage.transform.resize(img, shape)
          img_list.append(img)
      imgs = np.asarray(img_list, dtype=np.float32).transpose((0, 3, 1, 2)) - 128
      np.save('data/test_images_%d_%d.npy'%shape, imgs)
  
  def _get_data(shape):
      hash_test_img = "355e15800642286e7fe607d87c38aeeab085b0cc"
      hash_inception_v3 = "91807dfdbd336eb3b265dd62c2408882462752b9"
      utils.download("http://data.mxnet.io/data/test_images_%d_%d.npy" % (shape),
                     path="data/test_images_%d_%d.npy" % (shape),
                     sha1_hash=hash_test_img)
      utils.download("http://data.mxnet.io/data/inception-v3-dump.npz",
                     path='data/inception-v3-dump.npz',
                     sha1_hash=hash_inception_v3)
  
  @with_seed()
  def test_consistency(dump=False):
      shape = (299, 299)
      _get_model()
      _get_data(shape)
      if dump:
          _dump_images(shape)
          gt = None
      else:
          gt = {n: mx.nd.array(a) for n, a in np.load('data/inception-v3-dump.npz').items()}
      data = np.load('data/test_images_%d_%d.npy'%shape)
      sym, arg_params, aux_params = mx.model.load_checkpoint('model/Inception-7', 1)
      arg_params['data'] = data
      arg_params['softmax_label'] = np.random.randint(low=1, high=1000, size=(data.shape[0],))
      ctx_list = [{'ctx': mx.gpu(0), 'data': data.shape, 'type_dict': {'data': data.dtype}},
                  {'ctx': mx.cpu(0), 'data': data.shape, 'type_dict': {'data': data.dtype}}]
      gt = check_consistency(sym, ctx_list, arg_params=arg_params, aux_params=aux_params,
                             tol=1e-3, grad_req='null', raise_on_err=False, ground_truth=gt)
      if dump:
          np.savez('data/inception-v3-dump.npz', **{n: a.asnumpy() for n, a in gt.items()})
  
  if __name__ == '__main__':
      test_consistency(False)
  