text: #!/usr/bin/env python3
  """Calculates the Frechet Inception Distance (FID) to evalulate GANs
  
  The FID metric calculates the distance between two distributions of images.
  Typically, we have summary statistics (mean & covariance matrix) of one
  of these distributions, while the 2nd distribution is given by a GAN.
  
  When run as a stand-alone program, it compares the distribution of
  images that are stored as PNG/JPEG at a specified location with a
  distribution given by summary statistics (in pickle format).
  
  The FID is calculated by assuming that X_1 and X_2 are the activations of
  the pool_3 layer of the inception net for generated samples and real world
  samples respectively.
  
  See --help to see further details.
  
  Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead
  of Tensorflow
  
  Copyright 2018 Institute of Bioinformatics, JKU Linz
  
  Licensed 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 os
  import pathlib
  from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
  
  import numpy as np
  import torch
  from scipy import linalg
  from scipy.misc import imread
  from torch.nn.functional import adaptive_avg_pool2d
  
  try:
      from tqdm import tqdm
  except ImportError:
      # If not tqdm is not available, provide a mock version of it
      def tqdm(x): return x
  
  from inception import InceptionV3
  
  parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
  parser.add_argument('path', type=str, nargs=2,
                      help=('Path to the generated images or '
                            'to .npz statistic files'))
  parser.add_argument('--batch-size', type=int, default=50,
                      help='Batch size to use')
  parser.add_argument('--dims', type=int, default=2048,
                      choices=list(InceptionV3.BLOCK_INDEX_BY_DIM),
                      help=('Dimensionality of Inception features to use. '
                            'By default, uses pool3 features'))
  parser.add_argument('-c', '--gpu', default='', type=str,
                      help='GPU to use (leave blank for CPU only)')
  
  
  def get_activations(files, model, batch_size=50, dims=2048,
                      cuda=False, verbose=False):
      """Calculates the activations of the pool_3 layer for all images.
  
      Params:
      -- files       : List of image files paths
      -- model       : Instance of inception model
      -- batch_size  : Batch size of images for the model to process at once.
                       Make sure that the number of samples is a multiple of
                       the batch size, otherwise some samples are ignored. This
                       behavior is retained to match the original FID score
                       implementation.
      -- dims        : Dimensionality of features returned by Inception
      -- cuda        : If set to True, use GPU
      -- verbose     : If set to True and parameter out_step is given, the number
                       of calculated batches is reported.
      Returns:
      -- A numpy array of dimension (num images, dims) that contains the
         activations of the given tensor when feeding inception with the
         query tensor.
      """
      model.eval()
  
      if len(files) % batch_size != 0:
          print(('Warning: number of images is not a multiple of the '
                 'batch size. Some samples are going to be ignored.'))
      if batch_size > len(files):
          print(('Warning: batch size is bigger than the data size. '
                 'Setting batch size to data size'))
          batch_size = len(files)
  
      n_batches = len(files) // batch_size
      n_used_imgs = n_batches * batch_size
  
      pred_arr = np.empty((n_used_imgs, dims))
  
      for i in tqdm(range(n_batches)):
          if verbose:
              print('\rPropagating batch %d/%d' % (i + 1, n_batches),
                    end='', flush=True)
          start = i * batch_size
          end = start + batch_size
  
          images = np.array([imread(str(f)).astype(np.float32)
                             for f in files[start:end]])
  
          # Reshape to (n_images, 3, height, width)
          images = images.transpose((0, 3, 1, 2))
          images /= 255
  
          batch = torch.from_numpy(images).type(torch.FloatTensor)
          if cuda:
              batch = batch.cuda()
  
          pred = model(batch)[0]
  
          # If model output is not scalar, apply global spatial average pooling.
          # This happens if you choose a dimensionality not equal 2048.
          if pred.shape[2] != 1 or pred.shape[3] != 1:
              pred = adaptive_avg_pool2d(pred, output_size=(1, 1))
  
          pred_arr[start:end] = pred.cpu().data.numpy().reshape(batch_size, -1)
  
      if verbose:
          print(' done')
  
      return pred_arr
  
  
  def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
      """Numpy implementation of the Frechet Distance.
      The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
      and X_2 ~ N(mu_2, C_2) is
              d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
  
      Stable version by Dougal J. Sutherland.
  
      Params:
      -- mu1   : Numpy array containing the activations of a layer of the
                 inception net (like returned by the function 'get_predictions')
                 for generated samples.
      -- mu2   : The sample mean over activations, precalculated on an
                 representative data set.
      -- sigma1: The covariance matrix over activations for generated samples.
      -- sigma2: The covariance matrix over activations, precalculated on an
                 representative data set.
  
      Returns:
      --   : The Frechet Distance.
      """
  
      mu1 = np.atleast_1d(mu1)
      mu2 = np.atleast_1d(mu2)
  
      sigma1 = np.atleast_2d(sigma1)
      sigma2 = np.atleast_2d(sigma2)
  
      assert mu1.shape == mu2.shape, \
          'Training and test mean vectors have different lengths'
      assert sigma1.shape == sigma2.shape, \
          'Training and test covariances have different dimensions'
  
      diff = mu1 - mu2
  
      # Product might be almost singular
      covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
      if not np.isfinite(covmean).all():
          msg = ('fid calculation produces singular product; '
                 'adding %s to diagonal of cov estimates') % eps
          print(msg)
          offset = np.eye(sigma1.shape[0]) * eps
          covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
  
      # Numerical error might give slight imaginary component
      if np.iscomplexobj(covmean):
          if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
              m = np.max(np.abs(covmean.imag))
              raise ValueError('Imaginary component {}'.format(m))
          covmean = covmean.real
  
      tr_covmean = np.trace(covmean)
  
      return (diff.dot(diff) + np.trace(sigma1) +
              np.trace(sigma2) - 2 * tr_covmean)
  
  
  def calculate_activation_statistics(files, model, batch_size=50,
                                      dims=2048, cuda=False, verbose=False):
      """Calculation of the statistics used by the FID.
      Params:
      -- files       : List of image files paths
      -- model       : Instance of inception model
      -- batch_size  : The images numpy array is split into batches with
                       batch size batch_size. A reasonable batch size
                       depends on the hardware.
      -- dims        : Dimensionality of features returned by Inception
      -- cuda        : If set to True, use GPU
      -- verbose     : If set to True and parameter out_step is given, the
                       number of calculated batches is reported.
      Returns:
      -- mu    : The mean over samples of the activations of the pool_3 layer of
                 the inception model.
      -- sigma : The covariance matrix of the activations of the pool_3 layer of
                 the inception model.
      """
      act = get_activations(files, model, batch_size, dims, cuda, verbose)
      mu = np.mean(act, axis=0)
      sigma = np.cov(act, rowvar=False)
      return mu, sigma
  
  
  def _compute_statistics_of_path(path, model, batch_size, dims, cuda):
      if path.endswith('.npz'):
          f = np.load(path)
          m, s = f['mu'][:], f['sigma'][:]
          f.close()
      else:
          path = pathlib.Path(path)
          files = list(path.glob('*.jpg')) + list(path.glob('*.png'))
          m, s = calculate_activation_statistics(files, model, batch_size,
                                                 dims, cuda)
  
      return m, s
  
  
  def calculate_fid_given_paths(paths, batch_size, cuda, dims):
      """Calculates the FID of two paths"""
      for p in paths:
          if not os.path.exists(p):
              raise RuntimeError('Invalid path: %s' % p)
  
      block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
  
      model = InceptionV3([block_idx])
      if cuda:
          model.cuda()
  
      m1, s1 = _compute_statistics_of_path(paths[0], model, batch_size,
                                           dims, cuda)
      m2, s2 = _compute_statistics_of_path(paths[1], model, batch_size,
                                           dims, cuda)
      fid_value = calculate_frechet_distance(m1, s1, m2, s2)
  
      return fid_value
  
  
  # if __name__ == '__main__':
  #     args = parser.parse_args()
  #     os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
  
  #     fid_value = calculate_fid_given_paths(args.path,
  #                                           args.batch_size,
  #                                           args.gpu != '',
  #                                           args.dims)
  #     print('FID: ', fid_value)