Python tensorflow.compat.v1.where() Examples

def testTensorMultiplierOfGradient(self):
    gradient = tf.constant(self._grad_vec, dtype=tf.float32)
    variable = variables_lib.Variable(tf.zeros_like(gradient))
    multiplier_flag = variables_lib.Variable(True)
    tensor_multiplier = tf.where(multiplier_flag, self._multiplier, 1.0)
    grad_to_var = (gradient, variable)
    gradient_multipliers = {variable: tensor_multiplier}

    [grad_to_var] = learning.multiply_gradients([grad_to_var],
                                                gradient_multipliers)

    with self.cached_session() as sess:
      sess.run(variables_lib.global_variables_initializer())
      gradient_true_flag = sess.run(grad_to_var[0])
      sess.run(multiplier_flag.assign(False))
      gradient_false_flag = sess.run(grad_to_var[0])
    np_testing.assert_almost_equal(gradient_true_flag,
                                   self._multiplied_grad_vec, 5)
    np_testing.assert_almost_equal(gradient_false_flag, self._grad_vec, 5) 

def body(self, features):
    """Computes the targets' pre-logit activations given transformed inputs.

    Most `T2TModel` subclasses will override this method.

    Args:
      features: dict of str to Tensor, where each Tensor has shape [batch_size,
        ..., hidden_size]. It typically contains keys `inputs` and `targets`.

    Returns:
      output: Tensor of pre-logit activations with shape [batch_size, ...,
              hidden_size].
      losses: Either single loss as a scalar, a list, a Tensor (to be averaged),
              or a dictionary of losses. If losses is a dictionary with the key
              "training", losses["training"] is considered the final training
              loss and output is considered logits; self.top and self.loss will
              be skipped.
    """
    raise NotImplementedError("Abstract Method") 

def __init__(self, pad_mask):
    """Compute and store the location of the padding.

    Args:
      pad_mask (tf.Tensor): Reference padding tensor of shape
        [batch_size,length] or [dim_origin] (dim_origin=batch_size*length)
        containing non-zeros positive values to indicate padding location.
    """
    self.nonpad_ids = None
    self.dim_origin = None

    with tf.name_scope("pad_reduce/get_ids"):
      pad_mask = tf.reshape(pad_mask, [-1])  # Flatten the batch
      # nonpad_ids contains coordinates of zeros rows (as pad_mask is
      # float32, checking zero equality is done with |x| < epsilon, with
      # epsilon=1e-9 as standard, here pad_mask only contains positive values
      # so tf.abs would be redundant)
      self.nonpad_ids = tf.to_int32(tf.where(pad_mask < 1e-9))
      self.dim_origin = tf.shape(pad_mask)[:1] 

def _mix_tokens(p_sample, gold_targets, sampled_targets):
  """Interleave sampled and gold tokens randomly.

  Args:
    p_sample: float in [0, 1]. Probability a token will come from
      'sampled_targets'. 0 means all-gold, 1 means all-sampled.
    gold_targets: Tensor. Gold token IDs.
    sampled_targets: Tensor. Sampled token IDs. Same shape as 'gold_targets'.

  Returns:
    Tensor of same shape as 'gold_targets' containing a mix of tokens from
    'gold_targets' and 'sampled_targets'.
  """
  targets_shape = common_layers.shape_list(sampled_targets)
  return tf.where(
      tf.less(tf.random_uniform(targets_shape), p_sample),
      sampled_targets, gold_targets) 

def top_k_logits(logits, k):
    if k == 0:
        # no truncation
        return logits

    def _top_k():
        values, _ = tf.nn.top_k(logits, k=k)
        min_values = values[:, -1, tf.newaxis]
        return tf.where(
            logits < min_values,
            tf.ones_like(logits, dtype=logits.dtype) * -1e10,
            logits,
        )
    return tf.cond(
        tf.equal(k, 0),
        lambda: logits,
        lambda: _top_k(),
    ) 

def _randomized_roundoff_to_bfloat16(x, noise, cand1, cand2):
  """Round-off x to cand1 or to cand2 in an unbiased way.

  Cand1 and cand2 are the same shape as x.
  For every element of x, the corresponding elements of cand1 and cand2 should
  be the two closest bfloat16 values to x.  Order does not matter.
  cand1 and cand2 must differ from each other.

  Args:
    x: A float32 Tensor.
    noise: A Tensor broadcastable to the shape of x containing
    random uniform values in [0.0, 1.0].
    cand1: A bfloat16 Tensor the same shape as x.
    cand2: A bfloat16 Tensor the same shape as x.

  Returns:
    A bfloat16 Tensor.
  """
  cand1_f = tf.to_float(cand1)
  cand2_f = tf.to_float(cand2)
  step_size = cand2_f - cand1_f
  fpart = (x - cand1_f) / step_size
  ret = tf.where(tf.greater(fpart, noise), cand2, cand1)
  return ret 

def _to_bfloat16_unbiased(x, noise):
  """Convert a float32 to a bfloat16 using randomized roundoff.

  Args:
    x: A float32 Tensor.
    noise: a float32 Tensor with values in [0, 1), broadcastable to tf.shape(x)
  Returns:
    A float32 Tensor.
  """
  x_sign = tf.sign(x)
  # Make sure x is positive.  If it is zero, the two candidates are identical.
  x = x * x_sign + 1e-30
  cand1 = tf.to_bfloat16(x)
  cand1_f = tf.to_float(cand1)
  # This relies on the fact that for a positive bfloat16 b,
  # b * 1.005 gives you the next higher bfloat16 and b*0.995 gives you the
  # next lower one. Both 1.005 and 0.995 are ballpark estimation.
  cand2 = tf.to_bfloat16(
      tf.where(tf.greater(x, cand1_f), cand1_f * 1.005, cand1_f * 0.995))
  ret = _randomized_roundoff_to_bfloat16(x, noise, cand1, cand2)
  return ret * tf.to_bfloat16(x_sign) 

def neural_gpu_body(inputs, hparams, name=None):
  """The core Neural GPU."""
  with tf.variable_scope(name, "neural_gpu"):

    def step(state, inp):  # pylint: disable=missing-docstring
      x = tf.nn.dropout(state, 1.0 - hparams.dropout)
      for layer in range(hparams.num_hidden_layers):
        x = common_layers.conv_gru(
            x, (hparams.kernel_height, hparams.kernel_width),
            hparams.hidden_size,
            name="cgru_%d" % layer)
      # Padding input is zeroed-out in the modality, we check this by summing.
      padding_inp = tf.less(tf.reduce_sum(tf.abs(inp), axis=[1, 2]), 0.00001)
      new_state = tf.where(padding_inp, state, x)  # No-op where inp is padding.
      return new_state

    return tf.foldl(
        step,
        tf.transpose(inputs, [1, 0, 2, 3]),
        initializer=inputs,
        parallel_iterations=1,
        swap_memory=True) 

def _distributional_to_value(value_d, size, subscale, threshold):
  """Get a scalar value out of a value distribution in distributional RL."""
  half = size // 2
  value_range = (tf.to_float(tf.range(-half, half)) + 0.5) * subscale
  probs = tf.nn.softmax(value_d)

  if threshold == 0.0:
    return tf.reduce_sum(probs * value_range, axis=-1)

  # accumulated_probs[..., i] is the sum of probabilities in buckets upto i
  # so it is the probability that value <= i'th bucket value
  accumulated_probs = tf.cumsum(probs, axis=-1)
  # New probs are 0 on all lower buckets, until the threshold
  probs = tf.where(accumulated_probs < threshold, tf.zeros_like(probs), probs)
  probs /= tf.reduce_sum(probs, axis=-1, keepdims=True)  # Re-normalize.
  return tf.reduce_sum(probs * value_range, axis=-1) 

def __init__(self, num_experts, gates):
    """Create a SparseDispatcher.

    Args:
      num_experts: an integer.
      gates: a `Tensor` of shape `[batch_size, num_experts]`.

    Returns:
      a SparseDispatcher
    """
    self._gates = gates
    self._num_experts = num_experts

    where = tf.to_int32(tf.where(tf.transpose(gates) > 0))
    self._expert_index, self._batch_index = tf.unstack(where, num=2, axis=1)
    self._part_sizes_tensor = tf.reduce_sum(tf.to_int32(gates > 0), [0])
    self._nonzero_gates = tf.gather(
        tf.reshape(self._gates, [-1]),
        self._batch_index * num_experts + self._expert_index) 

def filter_correct_class(verifiable_obj, num_classes, labels, margin):
  """Filters out the objective when the target class contains the true label.

  Args:
    verifiable_obj: 2D tensor of shape (num_classes, batch_size) containing
      verifiable objectives.
    num_classes: number of target classes.
    labels: 1D tensor of shape (batch_size) containing the labels for each
      example in the batch.
    margin: Verifiable objective values for correct class will be forced to
      `-margin`, thus disregarding large negative bounds when maximising.

  Returns:
   2D tensor of shape (num_classes, batch_size) containing the corrected
   verifiable objective values for each (class, example).
  """
  targets_to_filter = tf.expand_dims(
      tf.range(num_classes, dtype=labels.dtype), axis=1)
  neq = tf.not_equal(targets_to_filter, labels)
  verifiable_obj = tf.where(neq, verifiable_obj, -margin *
                            tf.ones_like(verifiable_obj))
  return verifiable_obj 

def resize_and_crop_boxes(self):
    """Resize boxes and crop it to the self._output dimension."""
    boxlist = preprocessor.box_list.BoxList(self._boxes)
    boxes = preprocessor.box_list_scale(
        boxlist, self._scaled_height, self._scaled_width).get()
    # Adjust box coordinates based on the offset.
    box_offset = tf.stack([self._crop_offset_y, self._crop_offset_x,
                           self._crop_offset_y, self._crop_offset_x,])
    boxes -= tf.cast(tf.reshape(box_offset, [1, 4]), tf.float32)
    # Clip the boxes.
    boxes = self.clip_boxes(boxes)
    # Filter out ground truth boxes that are all zeros.
    indices = tf.where(tf.not_equal(tf.reduce_sum(boxes, axis=1), 0))
    boxes = tf.gather_nd(boxes, indices)
    classes = tf.gather_nd(self._classes, indices)
    return boxes, classes 

def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions)) 

def _match(self, similarity_matrix, **params):
    """Method to be overridden by implementations.

    Args:
      similarity_matrix: Float tensor of shape [N, M] with pairwise similarity
        where higher value means more similar.
      **params: Additional keyword arguments for specific implementations of
        the Matcher.

    Returns:
      match_results: Integer tensor of shape [M]: match_results[i]>=0 means
        that column i is matched to row match_results[i], match_results[i]=-1
        means that the column is not matched. match_results[i]=-2 means that
        the column is ignored (usually this happens when there is a very weak
        match which one neither wants as positive nor negative example).
    """
    pass 

def _build(self, x, state):
    prev_keep_mask = state
    shape = tf.shape(x)
    noise = tf.random_uniform(shape, dtype=x.dtype)
    other_mask = tf.floor(self._keep_prob + noise)
    choice_noise = tf.random_uniform(shape, dtype=x.dtype)
    choice = tf.less(choice_noise, self._flip_prob)
    # KLUDGE(melisgl): The client has to pass the last keep_mask from
    # a batch to the next so the mask may end up next to some
    # recurrent cell state. This state is often zero at the beginning
    # and may be periodically zeroed (per example) during training.
    # While zeroing LSTM state is okay, zeroing the dropout mask is
    # not. So instead of forcing every client to deal with this common
    # (?) case, if an all zero mask is detected, then regenerate a
    # fresh mask. This is of course a major hack and won't help with
    # learnt initial states, for example.
    sum_ = tf.reduce_sum(prev_keep_mask, 1, keepdims=True)
    is_initializing = tf.equal(sum_, 0.0)

    self._keep_mask = tf.where(tf.logical_or(choice, is_initializing),
                               other_mask,
                               prev_keep_mask)
    self._time_step += 1
    return x * self._keep_mask / self._keep_prob * self._scaler 

def unwrap(p, discont=np.pi, axis=-1):
  """Unwrap a cyclical phase tensor.

  Args:
    p: Phase tensor.
    discont: Float, size of the cyclic discontinuity.
    axis: Axis of which to unwrap.

  Returns:
    unwrapped: Unwrapped tensor of same size as input.
  """
  dd = diff(p, axis=axis)
  ddmod = tf.mod(dd + np.pi, 2.0 * np.pi) - np.pi
  idx = tf.logical_and(tf.equal(ddmod, -np.pi), tf.greater(dd, 0))
  ddmod = tf.where(idx, tf.ones_like(ddmod) * np.pi, ddmod)
  ph_correct = ddmod - dd
  idx = tf.less(tf.abs(dd), discont)
  ddmod = tf.where(idx, tf.zeros_like(ddmod), dd)
  ph_cumsum = tf.cumsum(ph_correct, axis=axis)

  shape = p.get_shape().as_list()
  shape[axis] = 1
  ph_cumsum = tf.concat([tf.zeros(shape, dtype=p.dtype), ph_cumsum], axis=axis)
  unwrapped = p + ph_cumsum
  return unwrapped 

def _encode_gif(images, fps):
  """Encodes numpy images into gif string.

  Args:
    images: A 4-D `uint8` `np.array` (or a list of 3-D images) of shape
      `[time, height, width, channels]` where `channels` is 1 or 3.
    fps: frames per second of the animation

  Returns:
    The encoded gif string.

  Raises:
    IOError: If the ffmpeg command returns an error.
  """
  writer = WholeVideoWriter(fps)
  writer.write_multi(images)
  return writer.finish() 

def scheduled_sample_prob(ground_truth_x,
                          generated_x,
                          batch_size,
                          scheduled_sample_var):
  """Probability based scheduled sampling.

  Args:
    ground_truth_x: tensor of ground-truth data points.
    generated_x: tensor of generated data points.
    batch_size: batch size
    scheduled_sample_var: probability of choosing from ground_truth.
  Returns:
    New batch with randomly selected data points.
  """
  probability_threshold = scheduled_sample_var
  probability_of_generated = tf.random_uniform([batch_size])
  return tf.where(probability_of_generated > probability_threshold,
                  generated_x, ground_truth_x) 

def vae(x, z_size, name=None):
  """Simple variational autoencoder without discretization.

  Args:
    x: Input to the discretization bottleneck.
    z_size: Number of bits, where discrete codes range from 1 to 2**z_size.
    name: Name for the bottleneck scope.

  Returns:
    Embedding function, latent, loss, mu and log_simga.
  """
  with tf.variable_scope(name, default_name="vae"):
    mu = tf.layers.dense(x, z_size, name="mu")
    log_sigma = tf.layers.dense(x, z_size, name="log_sigma")
    shape = common_layers.shape_list(x)
    epsilon = tf.random_normal([shape[0], shape[1], 1, z_size])
    z = mu + tf.exp(log_sigma / 2) * epsilon
    kl = 0.5 * tf.reduce_mean(
        tf.expm1(log_sigma) + tf.square(mu) - log_sigma, axis=-1)
    free_bits = z_size // 4
    kl_loss = tf.reduce_mean(tf.maximum(kl - free_bits, 0.0))
    return z, kl_loss, mu, log_sigma 

def frequency_weighted_cost_mask(peak=10.0, hz_flat=1000, sr=16000, n_fft=512):
  """Calculates a mask to weight lower frequencies higher.

  Piecewise linear approximation. Assumes magnitude is in log scale.
  Args:
    peak: Cost increase at 0 Hz.
    hz_flat: Hz at which cost increase is 0.
    sr: Sample rate.
    n_fft: FFT size.

  Returns:
    Constant tensor [1, N_freq, 1] of cost weighting.
  """
  n = int(n_fft / 2)
  cutoff = np.where(
      librosa.core.fft_frequencies(sr=sr, n_fft=n_fft) >= hz_flat)[0][0]
  mask = np.concatenate([np.linspace(peak, 1.0, cutoff), np.ones(n - cutoff)])
  return tf.constant(mask[np.newaxis, :, np.newaxis], dtype=tf.float32)


#---------------------------------------------------
# Neural Nets
#--------------------------------------------------- 

def _select_top_k(logits, top_k):
  """Replaces logits, expect the top k highest values, with small number (-1e6).

  If k is -1 don't replace anything.

  Args:
    logits: A `Tensor` of shape [batch_size, ..., vocab_size]
    top_k: vector of batch size.

  Returns:
    A `Tensor` with same shape  as logits.
  """
  vocab_size = logits.shape[-1]

  top_k = tf.where(
      tf.not_equal(top_k, -1), top_k,
      tf.ones_like(top_k) * vocab_size)

  return tf.where(
      tf.argsort(logits) < tf.reshape(top_k, [-1] + [1] *
                                      (len(logits.shape) - 1)), logits,
      tf.ones_like(logits) * -1e6) 

def _create_classification_targets(self, groundtruth_labels, match):
    """Create classification targets for each anchor.

    Assign a classification target of for each anchor to the matching
    groundtruth label that is provided by match.  Anchors that are not matched
    to anything are given the target self._unmatched_cls_target

    Args:
      groundtruth_labels:  a tensor of shape [num_gt_boxes, d_1, ... d_k]
        with labels for each of the ground_truth boxes. The subshape
        [d_1, ... d_k] can be empty (corresponding to scalar labels).
      match: a matcher.Match object that provides a matching between anchors
        and groundtruth boxes.

    Returns:
      a float32 tensor with shape [num_anchors, d_1, d_2 ... d_k], where the
      subshape [d_1, ..., d_k] is compatible with groundtruth_labels which has
      shape [num_gt_boxes, d_1, d_2, ... d_k].
    """
    return match.gather_based_on_match(
        groundtruth_labels,
        unmatched_value=self._unmatched_cls_target,
        ignored_value=self._unmatched_cls_target) 

def shape_list(x):
  """Return list of dims, statically where possible."""
  x = tf.convert_to_tensor(x)

  # If unknown rank, return dynamic shape
  if x.get_shape().dims is None:
    return tf.shape(x)

  static = x.get_shape().as_list()
  shape = tf.shape(x)

  ret = []
  for i, dim in enumerate(static):
    if dim is None:
      dim = shape[i]
    ret.append(dim)
  return ret 

def match(self, similarity_matrix, scope=None, **params):
    """Computes matches among row and column indices and returns the result.

    Computes matches among the row and column indices based on the similarity
    matrix and optional arguments.

    Args:
      similarity_matrix: Float tensor of shape [N, M] with pairwise similarity
        where higher value means more similar.
      scope: Op scope name. Defaults to 'Match' if None.
      **params: Additional keyword arguments for specific implementations of
        the Matcher.

    Returns:
      A Match object with the results of matching.
    """
    with tf.name_scope(scope, 'Match', [similarity_matrix, params]) as scope:
      return Match(self._match(similarity_matrix, **params)) 

def scale_gaussian_prior(name, z, logscale_factor=3.0, trainable=True):
  """Returns N(s^i * z^i, std^i) where s^i and std^i are pre-component.

  s^i is a learnable parameter with identity initialization.
  std^i is optionally learnable with identity initialization.

  Args:
    name: variable scope.
    z: input_tensor
    logscale_factor: equivalent to scaling up the learning_rate by a factor
                     of logscale_factor.
    trainable: Whether or not std^i is learnt.
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    z_shape = common_layers.shape_list(z)
    latent_multiplier = tf.get_variable(
        "latent_multiplier", shape=z_shape, dtype=tf.float32,
        initializer=tf.ones_initializer())
    log_scale = tf.get_variable(
        "log_scale_latent", shape=z_shape, dtype=tf.float32,
        initializer=tf.zeros_initializer(), trainable=trainable)
    log_scale = log_scale * logscale_factor
    return tfp.distributions.Normal(
        loc=latent_multiplier * z, scale=tf.exp(log_scale)) 

def accuracy_without_true_negatives(true_positives, false_positives,
                                    false_negatives):
  """Creates an op for calculating accuracy without true negatives.

  Args:
    true_positives: A tensor representing true_positives.
    false_positives: A tensor representing false_positives.
    false_negatives: A tensor representing false_negatives.

  Returns:
    A tensor with the result of the calculation.
  """
  return tf.where(
      tf.greater(true_positives + false_positives + false_negatives, 0),
      true_positives / (true_positives + false_positives + false_negatives), 0) 

def flatten_maybe_padded_sequences(maybe_padded_sequences, lengths=None):
  """Flattens the batch of sequences, removing padding (if applicable).

  Args:
    maybe_padded_sequences: A tensor of possibly padded sequences to flatten,
        sized `[N, M, ...]` where M = max(lengths).
    lengths: Optional length of each sequence, sized `[N]`. If None, assumes no
        padding.

  Returns:
     flatten_maybe_padded_sequences: The flattened sequence tensor, sized
         `[sum(lengths), ...]`.
  """
  def flatten_unpadded_sequences():
    # The sequences are equal length, so we should just flatten over the first
    # two dimensions.
    return tf.reshape(maybe_padded_sequences,
                      [-1] + maybe_padded_sequences.shape.as_list()[2:])

  if lengths is None:
    return flatten_unpadded_sequences()

  def flatten_padded_sequences():
    indices = tf.where(tf.sequence_mask(lengths))
    return tf.gather_nd(maybe_padded_sequences, indices)

  return tf.cond(
      tf.equal(tf.reduce_min(lengths), tf.shape(maybe_padded_sequences)[1]),
      flatten_unpadded_sequences,
      flatten_padded_sequences) 

def inv_mu_law_numpy(x, mu=255.0):
  """A numpy implementation of inverse Mu-Law.

  Args:
    x: The Mu-Law samples to decode.
    mu: The Mu we used to encode these samples.

  Returns:
    out: The decoded data.
  """
  x = np.array(x).astype(np.float32)
  out = (x + 0.5) * 2. / (mu + 1)
  out = np.sign(out) / mu * ((1 + mu)**np.abs(out) - 1)
  out = np.where(np.equal(x, 0), x, out)
  return out 

def gradient_histogram_summary(self, avg_grads):
    """Create histogram of log values of all non-zero gradients."""
    with tf.name_scope('log_gradients_summary'):
      all_grads = []
      for grad, _ in avg_grads:
        all_grads.append(tf.reshape(grad, [-1]))
      grads = tf.abs(tf.concat(all_grads, 0))
      # exclude grads with zero values.
      indices_for_non_zero_grads = tf.where(tf.not_equal(grads, 0))
      log_grads = tf.reshape(
          tf.log(tf.gather(grads, indices_for_non_zero_grads)), [-1])
      tf.summary.histogram('log_gradients', log_grads) 

def get_softmax_viz(image, softmax, nrows=None):
  """Arrange softmax maps in a grid and superimpose them on the image."""
  softmax_shape = tf.shape(softmax)
  batch_size = softmax_shape[0]
  target_height = softmax_shape[1] * 2
  target_width = softmax_shape[2] * 2
  num_points = softmax_shape[3]

  if nrows is None:
    # Find a number of rows such that the arrangement is as square as possible.
    num_points_float = tf.cast(num_points, tf.float32)
    nfsqrt = tf.cast(tf.floor(tf.sqrt(num_points_float)), tf.int32)
    divs = tf.range(1, nfsqrt + 1)
    remainders = tf.mod(num_points_float, tf.cast(divs, tf.float32))
    divs = tf.gather(divs, tf.where(tf.equal(remainders, 0)))
    nrows = tf.reduce_max(divs)
  ncols = tf.cast(num_points / nrows, tf.int32)
  nrows = tf.cast(nrows, tf.int32)
  # Normalize per channel
  img = softmax / tf.reduce_max(softmax, axis=[1, 2], keepdims=True)
  # Use softmax as hue and saturation and original image as value of HSV image.
  greyimg = tf.image.rgb_to_grayscale(image)
  greyimg = tf.image.resize_images(greyimg, [target_height, target_width])
  greyimg = tf.tile(greyimg, [1, 1, 1, num_points])
  greyimg = tf.reshape(greyimg,
                       [batch_size, target_height, target_width, num_points, 1])
  img = tf.image.resize_images(img, [target_height, target_width])
  img = tf.reshape(img,
                   [batch_size, target_height, target_width, num_points, 1])
  img = tf.concat([img / 2.0 + 0.5, img, greyimg * 0.7 + 0.3], axis=4)

  # Rearrange channels into a ncols x nrows grid.
  img = tf.reshape(img,
                   [batch_size, target_height, target_width, nrows, ncols, 3])
  img = tf.transpose(img, [0, 3, 1, 4, 2, 5])
  img = tf.reshape(img,
                   [batch_size, target_height * nrows, target_width * ncols, 3])

  img = tf.image.hsv_to_rgb(img)
  return img